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import numpy as np
import importlib
# fmt:off
import cost_matrix_generator as cmg
importlib.reload(cmg)
# fmt:on
class CostMatrixGenerator:
def __init__(self):
self.process_df = pd.DataFrame()
self.employee_usage = pd.DataFrame()
self.material_usage = pd.DataFrame()
self.capital_cost_usage = pd.DataFrame()
self.data_directory = "example"
def change_data_directory(self, new_directory):
self.data_directory = new_directory
def load_data(self):
self.process_df = pd.read_csv(
f"{self.data_directory}/generated_process_data.csv"
)
self.employee_usage = pd.read_csv(
f"{self.data_directory}/generated_employee_usage.csv"
)
self.material_usage = pd.read_csv(
f"{self.data_directory}/generated_material_usage.csv"
)
self.capital_cost_usage = pd.read_csv(
f"{self.data_directory}/generated_capital_cost.csv"
)
def remove_outlier_iqr(self, iqr_index=1.5):
process_df = self.process_df
q1 = np.percentile(process_df["cost"], 25)
q3 = np.percentile(process_df["cost"], 75)
iqr = q3 - q1
lower_bound = q1 - (iqr_index * iqr)
upper_bound = q3 + (iqr_index * iqr)
process_df = process_df[
(process_df["cost"] > lower_bound) & (
process_df["cost"] < upper_bound)
]
removed_data = self.process_df[
~self.process_df["process_id"].isin(process_df["process_id"])
]
self.process_df = process_df
print("Outliers removed")
print(f"Amount of process to remove {len(removed_data)}")
return process_df
def generate_data(self):
# Material
material_cost_matrix, material_amount_matrix = (
cmg.generate_material_usage_cost_matrix(
self.process_df, self.material_usage
)
)
material_cost_matrix = material_cost_matrix.values
material_amount_matrix = material_amount_matrix.values
row, col = material_cost_matrix.shape
material_cost_matrix = material_cost_matrix.reshape(row, 1, col)
material_amount_matrix = material_amount_matrix.reshape(row, 1, col)
# Employee
(
employee_cost_matrix,
employee_duration_matrix,
employee_day_amount_matrix,
) = cmg.generate_employee_usage_cost_matrix(
self.process_df, self.employee_usage
)
employee_cost_matrix = employee_cost_matrix.values
employee_duration_matrix = employee_duration_matrix.values
employee_day_amount_matrix = employee_day_amount_matrix.values
# Reshape Matrix
# Employee Cost
row, col = employee_cost_matrix.shape
employee_cost_matrix = employee_cost_matrix.reshape(
row, 1, col)
# Employee Duration
row, col = employee_duration_matrix.shape
employee_duration_matrix = employee_duration_matrix.reshape(
row, 1, col
)
# Employee Day Amount
row, col = employee_day_amount_matrix.shape
employee_day_amount_matrix = employee_day_amount_matrix.reshape(
row, 1, col
)
print(
f" Employee Cost matrix shape {employee_cost_matrix.shape}"
)
# Capital Cost
(
capital_cost_matrix,
day_amount_matrix,
capital_cost_duration_matrix,
) = cmg.generate_capital_cost_matrix(
self.process_df, capital_cost_df=self.capital_cost_usage
)
# Get Values
capital_cost_matrix = capital_cost_matrix.values
day_amount_matrix = day_amount_matrix.values
capital_cost_duration_matrix = capital_cost_duration_matrix.values
# Reshape Matrix
row, col = capital_cost_matrix.shape
capital_cost_matrix = capital_cost_matrix.reshape(row, 1, col)
row, col = day_amount_matrix.shape
day_amount_matrix = day_amount_matrix.reshape(row, 1, col)
row, col = capital_cost_duration_matrix.shape
capital_cost_duration_matrix = capital_cost_duration_matrix.reshape(
row, 1, col)
result_matrix = cmg.generate_price_matrix(
process_df=self.process_df, use_3d=True
)
return (
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 Finetuning
result_matrix,
)
def train_test_split(self, train_rate):
process_df_size = len(self.process_df)
trained_size = train_rate * process_df_size
train_process_df = self.process_df.sample(int(trained_size))
validate_process_df = self.process_df.drop(train_process_df.index)
# Ensure at least one record per original_material_name in train_process_df
unique_materials = self.process_df['original_material_name'].unique()
for material in unique_materials:
if material not in train_process_df['original_material_name'].values:
sample_record = self.process_df[self.process_df['original_material_name'] == material].sample(
1)
train_process_df = pd.concat([train_process_df, sample_record])
validate_process_df = validate_process_df.drop(
sample_record.index)
# Result Matrix
result_matrix = cmg.generate_price_matrix(
process_df=train_process_df, use_3d=True
)
validate_result_matrix = cmg.generate_price_matrix(
process_df=validate_process_df, use_3d=True
)
# Material
material_cost_matrix, material_amount_matrix = (
cmg.generate_material_usage_cost_matrix(
train_process_df, self.material_usage
)
)
material_cost_matrix = material_cost_matrix.values
material_amount_matrix = material_amount_matrix.values
# Material Validate
validate_material_cost_matrix, validate_material_amount_matrix = (
cmg.generate_material_usage_cost_matrix(
validate_process_df, self.material_usage
)
)
validate_material_cost_matrix = validate_material_cost_matrix.values
validate_material_amount_matrix = validate_material_amount_matrix.values
row, col = material_cost_matrix.shape
material_cost_matrix = material_cost_matrix.reshape(row, 1, col)
material_amount_matrix = material_amount_matrix.reshape(row, 1, col)
# Employee
(
employee_cost_matrix,
employee_duration_matrix,
employee_day_amount_matrix,
) = cmg.generate_employee_usage_cost_matrix(
train_process_df, self.employee_usage
)
# Employee Extract Value
employee_cost_matrix = employee_cost_matrix.values
employee_duration_matrix = employee_duration_matrix.values
employee_day_amount_matrix = employee_day_amount_matrix.values
# Employee Reshape
row, col = employee_cost_matrix.shape
employee_cost_matrix = employee_cost_matrix.reshape(
row, 1, col)
row, col = employee_duration_matrix.shape
employee_duration_matrix = employee_duration_matrix.reshape(
row, 1, col)
row, col = employee_day_amount_matrix.shape
employee_day_amount_matrix = employee_day_amount_matrix.reshape(
row, 1, col)
print(
f" Employee Cost matrix shape {employee_duration_matrix.shape} "
)
# Employee validate
(
validate_emp_cost_matrix,
validate_emp_duration_matrix,
validate_emp_day_amount_matrix,
) = cmg.generate_employee_usage_cost_matrix(
validate_process_df, self.employee_usage
)
# Employee Validate Extract Value
validate_emp_cost_matrix = validate_emp_cost_matrix.values
validate_emp_duration_matrix = validate_emp_duration_matrix.values
validate_emp_day_amount_matrix = validate_emp_day_amount_matrix.values
# Employee Validate Reshape
row, col = validate_emp_cost_matrix.shape
validate_emp_cost_matrix = validate_emp_cost_matrix.reshape(
row, 1, col
)
row, col = validate_emp_duration_matrix.shape
validate_emp_duration_matrix = validate_emp_duration_matrix.reshape(
row, 1, col
)
row, col = validate_emp_day_amount_matrix.shape
validate_emp_day_amount_matrix = validate_emp_day_amount_matrix.reshape(
row, 1, col
)
# Capital Cost
capital_cost_matrix, day_amount_matrix, capital_duration_matrix = (
cmg.generate_capital_cost_matrix(
train_process_df, capital_cost_df=self.capital_cost_usage
)
)
# Capital Cost Extract Value
capital_cost_matrix = capital_cost_matrix.values
day_amount_matrix = day_amount_matrix.values
capital_duration_matrix = capital_duration_matrix.values
# Capital Cost Reshape for 3D
row, col = capital_cost_matrix.shape
capital_cost_matrix = capital_cost_matrix.reshape(row, 1, col)
row, col = day_amount_matrix.shape
day_amount_matrix = day_amount_matrix.reshape(row, 1, col)
row, col = capital_duration_matrix.shape
capital_duration_matrix = capital_duration_matrix.reshape(row, 1, col)
# Capital Cost validate
(
valdiate_capital_cost_matrix,
validate_dayamount_matrix,
validate_capital_duration_matrix,
) = cmg.generate_capital_cost_matrix(
validate_process_df, capital_cost_df=self.capital_cost_usage
)
# Capital Cost Validate Value Extraction
valdiate_capital_cost_matrix = valdiate_capital_cost_matrix.values
validate_dayamount_matrix = validate_dayamount_matrix.values
validate_capital_duration_matrix = validate_capital_duration_matrix.values
# Capital Cost Validate Reshape
row, col = valdiate_capital_cost_matrix.shape
valdiate_capital_cost_matrix = valdiate_capital_cost_matrix.reshape(
row, 1, col)
row, col = validate_dayamount_matrix.shape
validate_dayamount_matrix = validate_dayamount_matrix.reshape(
row, 1, col)
row, col = validate_capital_duration_matrix.shape
validate_capital_duration_matrix = validate_capital_duration_matrix.reshape(
row, 1, col
)
validate_payload = {
"validate_process_df": validate_process_df,
"validate_result_matrix": validate_result_matrix,
"validate_material_cost_matrix": validate_material_cost_matrix,
"validate_material_amount_matrix": validate_material_amount_matrix,
"validate_capital_cost_matrix": valdiate_capital_cost_matrix,
"validate_day_amount_matrix": validate_dayamount_matrix,
"validate_capital_duration_matrix": validate_capital_duration_matrix,
"validate_employee_cost_matrix": validate_emp_cost_matrix,
"validate_employee_duration_matrix": validate_emp_duration_matrix,
"validate_employee_day_amount_matrix": validate_emp_day_amount_matrix,
}
return (
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,
validate_payload,
)
def train_test_split_without_matrix(self, train_rate):
process_df_size = len(self.process_df)
trained_size = train_rate * process_df_size
train_process_df = self.process_df.sample(int(trained_size))
validate_process_df = self.process_df.drop(train_process_df.index)
# Adjust to match new Process dataset
train_capital_cost = self.capital_cost_usage.copy()
train_capital_cost = train_capital_cost[
train_capital_cost["process_id"].isin(
train_process_df["process_id"])
]
train_employee_usage = self.employee_usage.copy()
train_employee_usage = train_employee_usage[
train_employee_usage["process_id"].isin(
train_process_df["process_id"])
]
train_material_usage = self.material_usage.copy()
train_material_usage = train_material_usage[
train_material_usage["process_id"].isin(
train_process_df["process_id"])
]
# Adjust to match new Process dataset for validation set
validate_capital_cost = self.capital_cost_usage.copy()
validate_capital_cost = validate_capital_cost[
validate_capital_cost["process_id"].isin(
validate_process_df["process_id"])
]
validate_employee_usage = self.employee_usage.copy()
validate_employee_usage = validate_employee_usage[
validate_employee_usage["process_id"].isin(
validate_process_df["process_id"]
)
]
validate_material_usage = self.material_usage.copy()
validate_material_usage = validate_material_usage[
validate_material_usage["process_id"].isin(
validate_process_df["process_id"]
)
]
return (
train_process_df,
train_employee_usage,
train_material_usage,
train_capital_cost,
validate_process_df,
validate_employee_usage,
validate_material_usage,
validate_capital_cost,
)
def generate_data_from_input(
self, process_df, material_usage, employee_usage, capital_cost_usage
):
# Material
material_cost_matrix, material_amount_matrix = (
cmg.generate_material_usage_cost_matrix(process_df, material_usage)
)
material_cost_matrix = material_cost_matrix.values
material_amount_matrix = material_amount_matrix.values
row, col = material_cost_matrix.shape
material_cost_matrix = material_cost_matrix.reshape(row, 1, col)
material_amount_matrix = material_amount_matrix.reshape(row, 1, col)
# Employee
(employee_cost_matrix,
employee_duration_matrix,
employee_day_amount_matrix,
) = cmg.generate_employee_usage_cost_matrix(process_df, employee_usage)
employee_cost_matrix = employee_cost_matrix.values
employee_duration_matrix = employee_duration_matrix.values
employee_day_amount_matrix = employee_day_amount_matrix.values
# Reshape Matrix
# Employee Cost
row, col = employee_cost_matrix.shape
employee_cost_matrix = employee_cost_matrix.reshape(
row, 1, col)
# Employee Duration
row, col = employee_duration_matrix.shape
employee_duration_matrix = employee_duration_matrix.reshape(
row, 1, col)
# Employee Day Amount
row, col = employee_day_amount_matrix.shape
employee_day_amount_matrix = employee_day_amount_matrix.reshape(
row, 1, col
)
print(
f" Employee Cost matrix shape {employee_cost_matrix.shape} "
)
# Capital Cost
(
capital_cost_matrix,
day_amount_matrix,
capital_cost_duration_matrix,
) = cmg.generate_capital_cost_matrix(
process_df, capital_cost_df=capital_cost_usage
)
# Get Values
capital_cost_matrix = capital_cost_matrix.values
day_amount_matrix = day_amount_matrix.values
capital_cost_duration_matrix = capital_cost_duration_matrix.values
# Reshape Matrix
row, col = capital_cost_matrix.shape
capital_cost_matrix = capital_cost_matrix.reshape(row, 1, col)
row, col = day_amount_matrix.shape
day_amount_matrix = day_amount_matrix.reshape(row, 1, col)
row, col = capital_cost_duration_matrix.shape
capital_cost_duration_matrix = capital_cost_duration_matrix.reshape(
row, 1, col)
result_matrix = cmg.generate_price_matrix(
process_df=process_df, use_3d=True)
return (
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 Finetuning
result_matrix,
)
def get_validation_payload(self, validate_process_df):
validate_result_matrix = cmg.generate_price_matrix(
process_df=validate_process_df, use_3d=True
)
# Material Validate
validate_material_cost_matrix, validate_material_amount_matrix = (
cmg.generate_material_usage_cost_matrix(
validate_process_df, self.material_usage
)
)
validate_material_cost_matrix = validate_material_cost_matrix.values
validate_material_amount_matrix = validate_material_amount_matrix.values
# Employee validate
(
validate_employee_cost_matrix,
validate_employee_duration_matrix,
validate_employee_day_amount_matrix,
) = cmg.generate_employee_usage_cost_matrix(
validate_process_df, self.employee_usage
)
# Employee Validate Extract Value
validate_employee_cost_matrix = validate_employee_cost_matrix.values
validate_employee_duration_matrix = validate_employee_duration_matrix.values
validate_employee_day_amount_matrix = validate_employee_day_amount_matrix.values
# Employee Validate Reshape
row, col = validate_employee_cost_matrix.shape
validate_employee_cost_matrix = validate_employee_cost_matrix.reshape(
row, 1, col
)
row, col = validate_employee_duration_matrix.shape
validate_employee_duration_matrix = validate_employee_duration_matrix.reshape(
row, 1, col
)
row, col = validate_employee_day_amount_matrix.shape
validate_employee_day_amount_matrix = validate_employee_day_amount_matrix.reshape(
row, 1, col
)
# Capital Cost validate
(
valdiate_capital_cost_matrix,
validate_dayamount_matrix,
validate_capital_duration_matrix,
) = cmg.generate_capital_cost_matrix(
validate_process_df, capital_cost_df=self.capital_cost_usage
)
# Capital Cost Validate Value Extraction
valdiate_capital_cost_matrix = valdiate_capital_cost_matrix.values
validate_dayamount_matrix = validate_dayamount_matrix.values
validate_capital_duration_matrix = validate_capital_duration_matrix.values
# Capital Cost Validate Reshape
row, col = valdiate_capital_cost_matrix.shape
valdiate_capital_cost_matrix = valdiate_capital_cost_matrix.reshape(
row, 1, col)
row, col = validate_dayamount_matrix.shape
validate_dayamount_matrix = validate_dayamount_matrix.reshape(
row, 1, col)
row, col = validate_capital_duration_matrix.shape
validate_capital_duration_matrix = validate_capital_duration_matrix.reshape(
row, 1, col
)
validate_payload = {
"validate_process_df": validate_process_df,
"validate_result_matrix": validate_result_matrix,
"validate_material_cost_matrix": validate_material_cost_matrix,
"validate_material_amount_matrix": validate_material_amount_matrix,
"validate_capital_cost_matrix": valdiate_capital_cost_matrix,
"validate_day_amount_matrix": validate_dayamount_matrix,
"validate_capital_duration_matrix": validate_capital_duration_matrix,
"validate_employee_cost_matrix": validate_employee_cost_matrix,
"validate_employee_duration_matrix": validate_employee_duration_matrix,
"validate_employee_day_amount_matrix": validate_employee_day_amount_matrix,
}
return validate_payload
def get_data(self):
return (
self.process_df,
self.employee_usage,
self.material_usage,
self.capital_cost_usage,
)
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