tested the pipeline for Fy676A

app.py

 import warnings
-from loguru import logger
+
+import numpy as np
 import pandas as pd
+from loguru import logger
 
 from scripts.constants.constants import RawConstants
+from scripts.core.model_loader import ModelLoader
 from scripts.section_utils.bof_section import preprocess_bof_section
 from scripts.section_utils.extruder_section import preprocess_extruder_section
 from scripts.section_utils.material_section import preprocess_viscosity_section
@@ -28,6 +31,66 @@ def read_raw_data(raw_path, raw_skip_rows):
     return df
 
 
+def merged_all_sections(sheet_df, mixer_df, extruder_df, bof_df, pickup_df, viscosity_df):
+    merged_df = pd.merge(sheet_df, mixer_df, on='batch-date', how='left')
+    merged_df = pd.merge(merged_df, extruder_df, on='batch-date', how='left')
+    merged_df = pd.merge(merged_df, bof_df, on='batch-date', how='left')
+    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[selected_cols]
+
+    viscosity_rubber_cols = ['Weight_type1', 'Weight_type2',
+                             'Weighted_PO_type', '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']
+    # Replace 0 values with NaN
+    for col in viscosity_rubber_cols:
+        df_grouped[col] = df_grouped[col].replace(0, np.nan)
+        df_grouped[col] = df_grouped[col].fillna(df_grouped[col].mean())
+
+    # Extract batch number and date
+    batch_number = df_grouped['batch-date'].str.extract(r'Batch_(\d+\.\d+)_')[0].astype(float)
+    date = pd.to_datetime(df_grouped['batch-date'].str.extract(r'_(\d{4}-\d{2}-\d{2})$')[0])
+
+    # Add extracted data as separate columns
+    df_grouped['Batch Number'] = batch_number
+    df_grouped['Date'] = date
+
+    # Sort by 'Batch Number' and 'Date'
+    df_grouped = df_grouped.sort_values(by=['Date', 'Batch Number'])
+    df_grouped = round(df_grouped, 6)
+    return df_grouped
+
+
+def load_and_predict(df_grouped, index_no):
+    if index_no == 1250:
+        logger.info(f"Loading model for {index_no}")
+        saved_model = ModelLoader({
+            "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_y = "viscosity"
+        features = df_grouped[cols_x]
+        labels = df_grouped[cols_y]
+        y_pred_full = saved_model.predict(features)
+        df_grouped['predicted_viscosity'] = y_pred_full
+        final_df = df_grouped[['Date', 'Batch Number', 'predicted_viscosity']]
+        final_df.to_csv('final_predicted_viscosity.csv')
+    elif index_no == 3294:
+        logger.info(f"Loading model for {index_no}")
+
+
 def start_prediction(raw_path, viscosity_path, index_no, raw_skip_rows, viscosity_skip_rows):
     logger.info("Reading raw file data")
     df = read_raw_data(raw_path, raw_skip_rows)
@@ -35,7 +98,7 @@ def start_prediction(raw_path, viscosity_path, index_no, raw_skip_rows, viscosit
 
     logger.info("Starting preprocessing material section")
     viscosity_df, raw_viscosity_df = preprocess_viscosity_section(viscosity_path, index_no, viscosity_skip_rows)
-    viscosity_df.to_csv('viscosity-agg.csv')
+    # viscosity_df.to_csv('viscosity-agg.csv')
     logger.info(f"The shape of the viscosity df is {viscosity_df.shape}")
     logger.info("Completed material section preprocessing")
 
@@ -43,35 +106,39 @@ def start_prediction(raw_path, viscosity_path, index_no, raw_skip_rows, viscosit
     df_sheet_grouped = preprocess_sheet_section(df, index_no)
     logger.info(f"The shape of the Sheet df is {df_sheet_grouped.shape}")
     logger.info("Completed sheet section preprocessing")
-    df_sheet_grouped.to_csv('sheet-agg.csv')
+    # df_sheet_grouped.to_csv('sheet-agg.csv')
 
     logger.info("Starting preprocessing mixer section")
     df_mixer_grouped = preprocess_mixer_section(df, index_no)
     logger.info(f"The shape of the Mixer df is {df_mixer_grouped.shape}")
     logger.info("Completed mixer section preprocessing")
-    df_mixer_grouped.to_csv('mixer-agg.csv')
+    # df_mixer_grouped.to_csv('mixer-agg.csv')
 
     logger.info("Starting preprocessing extruder section")
     df_extruder_grouped = preprocess_extruder_section(df, index_no, raw_viscosity_df)
     logger.info(f"The shape of the Extruder df is {df_extruder_grouped.shape}")
     logger.info("Completed extruder section preprocessing")
-    df_extruder_grouped.to_csv('extruder-agg.csv')
+    # df_extruder_grouped.to_csv('extruder-agg.csv')
 
     logger.info("Starting preprocessing bof section")
     df_bof_grouped = preprocess_bof_section(df, index_no, raw_viscosity_df)
     logger.info(f"The shape of the BOF df is {df_bof_grouped.shape}")
     logger.info("Completed bof section preprocessing")
-    df_bof_grouped.to_csv('bof-agg.csv')
-    bof_desc = df_bof_grouped.describe()
-    bof_desc.to_csv('bof-describe.csv')
+    # df_bof_grouped.to_csv('bof-agg.csv')
+    # bof_desc = df_bof_grouped.describe()
+    # bof_desc.to_csv('bof-describe.csv')
 
     logger.info("Starting preprocessing pickup section")
     df_pickup_grouped = preprocess_pickup_section(df, index_no, raw_viscosity_df)
     logger.info(f"The shape of the Extruder df is {df_pickup_grouped.shape}")
     logger.info("Completed pickup section preprocessing")
-    df_pickup_grouped.to_csv('pickup-agg.csv')
-    df = pd.read_csv('pickup-agg.csv')
-    print(df.describe())
+    # df_pickup_grouped.to_csv('pickup-agg.csv')
+    # df = pd.read_csv('pickup-agg.csv')
+    # print(df.describe())
+    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)
 
 
 if __name__ == "__main__":

models/fy676a/MLmodel

+flavors:
+  python_function:
+    env: conda.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    python_version: 3.10.13
+  sklearn:
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 1.2.2
+utc_time_created: '2023-12-20 06:24:57.321465'

models/fy676a/conda.yaml

+channels:
+- conda-forge
+dependencies:
+- python=3.10.13
+- pip
+- pip:
+  - mlflow
+  - cloudpickle==3.0.0
+  - scikit-learn==1.2.2
+name: mlflow-env

models/fy676a/requirements.txt

+mlflow
+cloudpickle==3.0.0
+scikit-learn==1.2.2
\ No newline at end of file

scripts/core/model_trainer.py

@@ -33,7 +33,7 @@ def model_trainer():
     merged_df = pd.merge(merged_df, bof_df, on='batch-date', how='left')
     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')
-    print(df_grouped.columns)
+    # print(df_grouped.columns)
     selected_cols = df_grouped.columns
     df_grouped = df_grouped[selected_cols]