updated the fy676a model

app.py

@@ -21,14 +21,16 @@ warnings.filterwarnings("ignore")
 def model_trainer(df_grouped, index_no):
     cols_x, cols_y, saved_model = None, None, None
     if index_no == 1250:
-        cols_x = ['temperature_ws_side_std', 'calender_roll_upper_side_inlet_side_cooling_water_temperature_mean',
-                  '_calendar_current_mean', 'electric_energy_mean', 'seat_temperature_immediately_after_bof_mean',
-                  'Weighted_NITROGEN_type', 'ram_pressure_mean', 'surface_temperature_center_std',
-                  'drilled_side_left_exit_side_cooling_water_temperature_mean', 'Weighted_VM_type',
-                  'screw_operation_side_outlet_side_cooling_water_flow_rate_std', 'Weighted_DIRT_type',
-                  'screw_opposite_operation_side_outlet_side_cooling_water_temperature_std', 'residence_time_max',
-                  'calender_roll_lower_side_inlet_side_cooling_water_flow_rate_mean', 'Weighted_ASH_type',
-                  'Weighted_PO_type', 'drilled_side_right_exit_side_cooling_water_flow_rate_std']
+        cols_x = ['temperature_ws_side_std', '_calendar_current_mean', 'Weighted_NITROGEN_type', 'ram_pressure_mean',
+                  'electric_energy_mean', 'screw_operation_side_outlet_side_cooling_water_flow_rate_std',
+                  'calender_roll_upper_side_inlet_side_cooling_water_temperature_mean', 'Weighted_VM_type',
+                  'seat_temperature_immediately_after_bof_mean', 'Weighted_DIRT_type', 'surface_temperature_center_std',
+                  'residence_time_max', 'drilled_side_left_exit_side_cooling_water_temperature_mean',
+                  'Weighted_PRI_type', 'calender_roll_lower_side_inlet_side_cooling_water_flow_rate_mean',
+                  'screw_opposite_operation_side_outlet_side_cooling_water_temperature_std', 'Weighted_ASH_type',
+                  'Weighted_PO_type', 'mixer_rotor_right_inlet_side_cooling_water_flow_rate_mean',
+                  'drilled_side_right_exit_side_cooling_water_flow_rate_std',
+                  'Weighted_Humidity during transportation__type[%]']
         cols_y = "viscosity"
         saved_model = ModelLoader({
             "type": "mlflow.sklearn",
@@ -116,6 +118,7 @@ def merged_all_sections(sheet_df, mixer_df, extruder_df, bof_df, pickup_df, visc
     merged_df = pd.merge(merged_df, pickup_df, on='batch-date', how='left')
     df_grouped = pd.merge(merged_df, viscosity_df, on='batch-date', how='left')
     selected_cols = df_grouped.columns
+    df_grouped = df_grouped[df_grouped['status'] == True]
     df_grouped = df_grouped[selected_cols]
 
     viscosity_rubber_cols = ['Weight_type1', 'Weight_type2',
@@ -150,14 +153,16 @@ def load_and_predict(df_grouped, index_no):
             "type": "mlflow.sklearn",
             "path": "models/fy676a"
         }).load_model()
-        cols_x = ['temperature_ws_side_std', 'calender_roll_upper_side_inlet_side_cooling_water_temperature_mean',
-                  '_calendar_current_mean', 'electric_energy_mean', 'seat_temperature_immediately_after_bof_mean',
-                  'Weighted_NITROGEN_type', 'ram_pressure_mean', 'surface_temperature_center_std',
-                  'drilled_side_left_exit_side_cooling_water_temperature_mean', 'Weighted_VM_type',
-                  'screw_operation_side_outlet_side_cooling_water_flow_rate_std', 'Weighted_DIRT_type',
-                  'screw_opposite_operation_side_outlet_side_cooling_water_temperature_std', 'residence_time_max',
-                  'calender_roll_lower_side_inlet_side_cooling_water_flow_rate_mean', 'Weighted_ASH_type',
-                  'Weighted_PO_type', 'drilled_side_right_exit_side_cooling_water_flow_rate_std']
+        cols_x = ['temperature_ws_side_std', '_calendar_current_mean', 'Weighted_NITROGEN_type', 'ram_pressure_mean',
+                  'electric_energy_mean', 'screw_operation_side_outlet_side_cooling_water_flow_rate_std',
+                  'calender_roll_upper_side_inlet_side_cooling_water_temperature_mean', 'Weighted_VM_type',
+                  'seat_temperature_immediately_after_bof_mean', 'Weighted_DIRT_type', 'surface_temperature_center_std',
+                  'residence_time_max', 'drilled_side_left_exit_side_cooling_water_temperature_mean',
+                  'Weighted_PRI_type', 'calender_roll_lower_side_inlet_side_cooling_water_flow_rate_mean',
+                  'screw_opposite_operation_side_outlet_side_cooling_water_temperature_std', 'Weighted_ASH_type',
+                  'Weighted_PO_type', 'mixer_rotor_right_inlet_side_cooling_water_flow_rate_mean',
+                  'drilled_side_right_exit_side_cooling_water_flow_rate_std',
+                  'Weighted_Humidity during transportation__type[%]']
         cols_y = "viscosity"
         features = df_grouped[cols_x]
         labels = df_grouped[cols_y]
@@ -242,8 +247,8 @@ def start_prediction(raw_path, viscosity_path, index_no, raw_skip_rows, viscosit
     df_grouped = merged_all_sections(df_sheet_grouped, df_mixer_grouped, df_extruder_grouped, df_bof_grouped,
                                      df_pickup_grouped, viscosity_df)
 
-    load_and_predict(df_grouped, index_no)
-    # model_trainer(df_grouped, index_no)
+    # load_and_predict(df_grouped, index_no)
+    model_trainer(df_grouped, index_no)
 
 
 if __name__ == "__main__":

models/fy676a/MLmodel

@@ -8,4 +8,4 @@ flavors:
     pickled_model: model.pkl
     serialization_format: cloudpickle
     sklearn_version: 1.2.2
-utc_time_created: '2023-12-20 06:24:57.321465'
+utc_time_created: '2023-12-21 10:42:57.059987'

scripts/constants/constants.py

@@ -191,7 +191,7 @@ class ViscosityConstants:
         'Weighted_DIRT_type', 'Weighted_ASH_type', 'Weighted_VM_type',
         'Weighted_PRI_type', 'Weighted_NITROGEN_type',
         'Weighted_Temperature during transportation_type[℃]',
-        'Weighted_Humidity during transportation__type[%]', 'Weighted Sum', 'viscosity']
+        'Weighted_Humidity during transportation__type[%]', 'Weighted Sum', 'viscosity','status']
 
 
 class SheetConstants:

scripts/section_utils/material_section.py

@@ -6,6 +6,21 @@ from scripts.constants.constants import ViscosityConstants
 warnings.filterwarnings("ignore")
 
 
+def create_status_column(df, type_col_name, columns_list):
+    status_col = []
+
+    for i, val in enumerate(df[type_col_name]):
+        if val == 0:
+            status_col.append(False)
+        else:
+            if any(df[column].iloc[i] == 0 for column in columns_list):
+                status_col.append(False)
+            else:
+                status_col.append(True)
+
+    return status_col
+
+
 def preprocess_viscosity_section(viscosity_df, index_number):
     # adding date col to the viscosity df
     viscosity_df = viscosity_df.sort_values(by='Mixing date')
@@ -68,6 +83,27 @@ def preprocess_viscosity_section(viscosity_df, index_number):
     viscosity_df = viscosity_df[new_order]
     viscosity_df['batch-date'] = 'Batch_' + viscosity_df['Batch No.'].astype(str) + '_' + viscosity_df['date'].astype(
         str)
+    # Added Status to check rubber
+    # Rubber Type 1
+    rubber_1_cols = [
+                     'DIRT_type1',
+                     'ASH_type1',
+                     'VM_type1',
+                     'PRI_type1',
+                     'NITROGEN_type1'
+                     ]
+    # Rubber Type 2
+    rubber_2_cols = [
+                     'PO_type2',
+                     'DIRT_type1',
+                     'ASH_type2',
+                     'VM_type2',
+                     'PRI_type2',
+                     'NITROGEN_type2'
+                     ]
+    viscosity_df['rubber_status_1'] = create_status_column(viscosity_df, 'Weight_type1', rubber_1_cols)
+    viscosity_df['rubber_status_2'] = create_status_column(viscosity_df, 'Weight_type2', rubber_2_cols)
+    viscosity_df['status'] = viscosity_df['rubber_status_1'] | viscosity_df['rubber_status_2']
     req_cols = ViscosityConstants.req_cols
     final_viscosity_df = viscosity_df[req_cols]
     final_viscosity_df = round(final_viscosity_df, 6)