| import numpy as np |
|
|
|
|
| def get_sample_payload( |
| sample_payload, |
| material_element, |
| employee_element, |
| capital_cost_element, |
| material_cost_input, |
| material_amount_input, |
| employee_input, |
| emp_dur_input, |
| employee_dayamount_input, |
| capital_cost_input, |
| day_amount_input, |
| capital_cost_dur_input, |
| predicted_mc, |
| predicted_ec, |
| predicted_cc, |
| bias, |
| result_input, |
| result, |
| error, |
| percent_loss, |
| epoch_number, |
| sample_number, |
| model_weights, |
| ): |
| |
| |
| |
| i = epoch_number |
| j = sample_number |
| model_bias = bias |
|
|
| material_element.check_type("material") |
| material_elem_weight = material_element.get_weights() |
| x, y, z = np.array(material_elem_weight).shape |
| material_elem_weight = np.array(material_elem_weight).reshape(x, y) |
| |
| material_elem_bias = material_element.get_biases() |
| x, y, z = np.array(material_elem_bias).shape |
| material_elem_bias = np.array(material_elem_bias).reshape(x, y) |
| employee_element.check_type("employee") |
| |
| employee_elem_weight = employee_element.get_weights() |
| x, y, z = np.array(employee_elem_weight).shape |
| employee_elem_weight = np.array(employee_elem_weight).reshape(x, y) |
| |
| employee_elem_bias = employee_element.get_biases() |
| x, y, z = np.array(employee_elem_bias).shape |
| employee_elem_bias = np.array(employee_elem_bias).reshape(x, y) |
| |
| capital_cost_element.check_type("capital") |
| capital_elem_weight = capital_cost_element.get_weights() |
| x, y, z = np.array(capital_elem_weight).shape |
| capital_elem_weight = np.array(capital_elem_weight).reshape(x, y) |
| |
| capital_elem_bias = capital_cost_element.get_biases() |
| x, y, z = np.array(capital_elem_bias).shape |
| capital_elem_bias = np.array(capital_elem_bias).reshape(x, y) |
| sample_payload.append( |
| { |
| "epoch": (i - 1), |
| "sample": j, |
| "material_cost": material_cost_input, |
| "material_amount": material_amount_input, |
| "material_weight": material_elem_weight, |
| "material_bias": material_elem_bias, |
| "employee_cost": employee_input, |
| "employee_duration": emp_dur_input, |
| "employee_dayamount": employee_dayamount_input, |
| "employee_weight": employee_elem_weight, |
| "employee_bias": employee_elem_bias, |
| "capital_cost": capital_cost_input, |
| "day_amount": day_amount_input, |
| "capital_cost_duration": capital_cost_dur_input, |
| "capital_cost_weight": capital_elem_weight, |
| "capital_cost_bias": capital_elem_bias, |
| "result": result_input, |
| "result_predict": result, |
| } |
| ) |
| material_costs = material_cost_input.flatten() |
| for idx, cost in enumerate(material_costs): |
| sample_payload[-1][f"material_cost_{idx + 1}"] = cost |
| material_amounts = material_amount_input.flatten() |
| for idx, amount in enumerate(material_amounts): |
| sample_payload[-1][f"material_amount_{idx + 1}"] = amount |
| material_weights = material_elem_weight.flatten() |
| for idx, weight in enumerate(material_weights): |
| sample_payload[-1][f"material_weight_{idx + 1}"] = weight |
| material_biases = material_elem_bias.flatten() |
| for idx, bias in enumerate(material_biases): |
| sample_payload[-1][f"material_bias_{idx + 1}"] = bias |
| employee_costs = employee_input.flatten() |
| for idx, cost in enumerate(employee_costs): |
| sample_payload[-1][f"employee_cost_{idx + 1}"] = cost |
| employee_durations = emp_dur_input.flatten() |
| for idx, duration in enumerate(employee_durations): |
| sample_payload[-1][f"employee_duration_{idx + 1}"] = duration |
| employee_dayamounts = employee_dayamount_input.flatten() |
| for idx, amount in enumerate(employee_dayamounts): |
| sample_payload[-1][f"employee_dayamount_{idx + 1}"] = amount |
| employee_weights = employee_elem_weight.flatten() |
| for idx, weight in enumerate(employee_weights): |
| sample_payload[-1][f"employee_weight_{idx + 1}"] = weight |
| employee_biases = employee_elem_bias.flatten() |
| for idx, bias in enumerate(employee_biases): |
| sample_payload[-1][f"employee_bias_{idx + 1}"] = bias |
| capital_costs = capital_cost_input.flatten() |
| for idx, cost in enumerate(capital_costs): |
| sample_payload[-1][f"capital_cost_{idx + 1}"] = cost |
| day_amounts = day_amount_input.flatten() |
| for idx, amount in enumerate(day_amounts): |
| sample_payload[-1][f"day_amount_{idx + 1}"] = amount |
| capital_cost_durations = capital_cost_dur_input.flatten() |
| for idx, duration in enumerate(capital_cost_durations): |
| sample_payload[-1][f"capital_cost_duration_{idx + 1}"] = duration |
| capital_cost_weights = capital_elem_weight.flatten() |
| for idx, weight in enumerate(capital_cost_weights): |
| sample_payload[-1][f"capital_cost_weight_{idx + 1}"] = weight |
| capital_cost_bias = capital_elem_bias.flatten() |
| for idx, bias in enumerate(capital_cost_bias): |
| sample_payload[-1][f"capital_cost_bias_{idx + 1}"] = bias |
| predicted_mc_reshape = np.array(predicted_mc).flatten() |
| for idx, res in enumerate(predicted_mc_reshape): |
| sample_payload[-1]["total_material"] = res |
| predicted_ec_reshape = np.array(predicted_ec).flatten() |
| for idx, res in enumerate(predicted_ec_reshape): |
| sample_payload[-1]["total_daily_employee"] = res |
| predicted_cc_reshape = np.array(predicted_cc).flatten() |
| for idx, res in enumerate(predicted_cc_reshape): |
| sample_payload[-1]["total_capital_cost"] = res |
|
|
| model_bias_reshape = np.array(model_bias).flatten() |
| for idx, res in enumerate(model_bias_reshape): |
| sample_payload[-1]["model_bias"] = res |
|
|
| model_weights_reshape = np.array(model_weights).flatten() |
| for idx, res in enumerate(model_weights_reshape): |
| sample_payload[-1][f"model_weight_{idx + 1}"] = res |
| result_reshape = np.array(result_input).flatten() |
| for idx, res in enumerate(result_reshape): |
| sample_payload[-1]["result"] = res |
| result_predict_reshape = np.array(result).flatten() |
| for idx, res in enumerate(result_predict_reshape): |
| sample_payload[-1]["result_predict"] = res |
| sample_payload[-1]["error"] = error |
| sample_payload[-1]["error_percent"] = percent_loss |
|
|
| return sample_payload |
|
|
