Update domain_space.py

domain_space.py

@@ -3,129 +3,130 @@ import pandas as pd
 import os
 import matplotlib.pyplot as plt
 
-from utils import unpickle_file, scale_numerical_w_missing
+# from utils import unpickle_file, scale_numerical_w_missing
+from utils import unpickle_file
 import plotly.express as px
 from gradio_utils import load_theme
-from alloy_data_preprocessing import add_physics_features
-from inference_model_main import predict_all_results
+# from alloy_data_preprocessing import add_physics_features
+# from inference_model_main import predict_all_results
 import plotly.graph_objects as go
 import yaml
 import numpy as np
 
 
-def run_predictions(
-    df,
-    scaler_inputs_path,
-    main_model_path,
-    main_input_cols_order,
-    intermediate_model_path,
-    intermediate_results_columns,
-):
-    """
-    Scale the data and runs the predictions on the intermediate columns and the final properties
-    """
-    scaler_inputs = unpickle_file(scaler_inputs_path)
-    df_p = add_physics_features(df)
-    df_scaled = scale_numerical_w_missing(df_p, scaler_inputs.feature_names_in_, scaler_inputs)
-
-    y_pred, uncertainty = predict_all_results(
-        df_scaled,
-        main_model_path,
-        main_input_cols_order,
-        scaler_targets_main=None,
-        intermediate_model_path=intermediate_model_path,
-        intermediate_results_columns=intermediate_results_columns,
-        return_uncertainty=True,
-        uncertainty_type="weighted",
-    )
-
-    return y_pred, uncertainty
-
-
-def create_domain_space(space_dict, inference_dict, df_path):
-    """
-    Create the dataframe containing the pre-computed values for the uncertainty
-    """
-    input_cols = ["%C", "%Co", "%Cr", "%V", "%Mo", "%W", "Temperature_C"]
-
-    c = space_dict["%C"]["value"]
-    co = space_dict["%Co"]["value"]
-    cr = space_dict["%Cr"]["value"]
-    v = space_dict["%V"]["value"]
-    mo = space_dict["%Mo"]["value"]
-    w = space_dict["%W"]["value"]
-    temp = 538
-    space_list = [
-        [ic, ico, icr, iv, imo, iw, temp]
-        for ic in np.arange(
-            space_dict["%C"]["min"], space_dict["%C"]["max"] + space_dict["%C"]["step"], space_dict["%C"]["step"]
-        )
-        for ico in np.arange(
-            space_dict["%Co"]["min"], space_dict["%Co"]["max"] + space_dict["%Co"]["step"], space_dict["%Co"]["step"]
-        )
-        for icr in np.arange(
-            space_dict["%Cr"]["min"], space_dict["%Cr"]["max"] + space_dict["%Cr"]["step"], space_dict["%Cr"]["step"]
-        )
-        for iv in np.arange(
-            space_dict["%V"]["min"], space_dict["%V"]["max"] + space_dict["%V"]["step"], space_dict["%V"]["step"]
-        )
-        for imo in np.arange(
-            space_dict["%Mo"]["min"], space_dict["%Mo"]["max"] + space_dict["%Mo"]["step"], space_dict["%Mo"]["step"]
-        )
-        for iw in np.arange(
-            space_dict["%W"]["min"], space_dict["%W"]["max"] + space_dict["%W"]["step"], space_dict["%W"]["step"]
-        )
-    ]
-
-    df_synth = pd.DataFrame(space_list, columns=input_cols)
-
-    print("Uncertainty space will be computed on:")
-    print(df_synth.shape)
-
-    model_path = inference_dict["final_prediction"]["model_path"]
-    print("Model used:", model_path)
-    scaler_inputs_intermediate = inference_dict["scaler_inputs_path"]
-    intermediate_cols = [
-        "%C matrice",
-        "%Co matrice",
-        "%Cr matrice",
-        "%V matrice",
-        "%Mo matrice",
-        "%W matrice",
-        "M6C",
-        "M23C6",
-        "FCCA1#2",
-        "M2C",
-        "MC - SHP",
-        "MC ETA",
-    ]
-    scaler_inputs_main = unpickle_file(inference_dict["final_prediction"]["scaler_inputs_path"])
-    intermediate_model_path_dict = inference_dict["multiple_model_path"]
-
-    y_pred, uncertainty = run_predictions(
-        df_synth,
-        scaler_inputs_intermediate,
-        model_path,
-        scaler_inputs_main.feature_names_in_,
-        intermediate_model_path_dict,
-        intermediate_cols,
-    )
-    df_synth_pred = df_synth.copy()
-    df_synth_pred["y_pred"] = y_pred
-    df_synth_pred["uncertainty_not_scaled"] = uncertainty
-    min_uncertainty, max_uncertainty = (
-        df_synth_pred["uncertainty_not_scaled"].min(),
-        df_synth_pred["uncertainty_not_scaled"].max(),
-    )
-    df_synth_pred["uncertainty"] = (df_synth_pred["uncertainty_not_scaled"] - min_uncertainty) / (
-        max_uncertainty - min_uncertainty
-    )
-    print("Domain space created")
-
-    print("-----------------------------")
-    print("Saving dataframe at", df_path)
-    df_synth_pred.to_csv(df_path, sep=";", index=False)
-    return df_synth_pred
+# def run_predictions(
+#     df,
+#     scaler_inputs_path,
+#     main_model_path,
+#     main_input_cols_order,
+#     intermediate_model_path,
+#     intermediate_results_columns,
+# ):
+#     """
+#     Scale the data and runs the predictions on the intermediate columns and the final properties
+#     """
+#     scaler_inputs = unpickle_file(scaler_inputs_path)
+#     df_p = add_physics_features(df)
+#     df_scaled = scale_numerical_w_missing(df_p, scaler_inputs.feature_names_in_, scaler_inputs)
+
+#     y_pred, uncertainty = predict_all_results(
+#         df_scaled,
+#         main_model_path,
+#         main_input_cols_order,
+#         scaler_targets_main=None,
+#         intermediate_model_path=intermediate_model_path,
+#         intermediate_results_columns=intermediate_results_columns,
+#         return_uncertainty=True,
+#         uncertainty_type="weighted",
+#     )
+
+#     return y_pred, uncertainty
+
+
+# def create_domain_space(space_dict, inference_dict, df_path):
+#     """
+#     Create the dataframe containing the pre-computed values for the uncertainty
+#     """
+#     input_cols = ["%C", "%Co", "%Cr", "%V", "%Mo", "%W", "Temperature_C"]
+
+#     c = space_dict["%C"]["value"]
+#     co = space_dict["%Co"]["value"]
+#     cr = space_dict["%Cr"]["value"]
+#     v = space_dict["%V"]["value"]
+#     mo = space_dict["%Mo"]["value"]
+#     w = space_dict["%W"]["value"]
+#     temp = 538
+#     space_list = [
+#         [ic, ico, icr, iv, imo, iw, temp]
+#         for ic in np.arange(
+#             space_dict["%C"]["min"], space_dict["%C"]["max"] + space_dict["%C"]["step"], space_dict["%C"]["step"]
+#         )
+#         for ico in np.arange(
+#             space_dict["%Co"]["min"], space_dict["%Co"]["max"] + space_dict["%Co"]["step"], space_dict["%Co"]["step"]
+#         )
+#         for icr in np.arange(
+#             space_dict["%Cr"]["min"], space_dict["%Cr"]["max"] + space_dict["%Cr"]["step"], space_dict["%Cr"]["step"]
+#         )
+#         for iv in np.arange(
+#             space_dict["%V"]["min"], space_dict["%V"]["max"] + space_dict["%V"]["step"], space_dict["%V"]["step"]
+#         )
+#         for imo in np.arange(
+#             space_dict["%Mo"]["min"], space_dict["%Mo"]["max"] + space_dict["%Mo"]["step"], space_dict["%Mo"]["step"]
+#         )
+#         for iw in np.arange(
+#             space_dict["%W"]["min"], space_dict["%W"]["max"] + space_dict["%W"]["step"], space_dict["%W"]["step"]
+#         )
+#     ]
+
+#     df_synth = pd.DataFrame(space_list, columns=input_cols)
+
+#     print("Uncertainty space will be computed on:")
+#     print(df_synth.shape)
+
+#     model_path = inference_dict["final_prediction"]["model_path"]
+#     print("Model used:", model_path)
+#     scaler_inputs_intermediate = inference_dict["scaler_inputs_path"]
+#     intermediate_cols = [
+#         "%C matrice",
+#         "%Co matrice",
+#         "%Cr matrice",
+#         "%V matrice",
+#         "%Mo matrice",
+#         "%W matrice",
+#         "M6C",
+#         "M23C6",
+#         "FCCA1#2",
+#         "M2C",
+#         "MC - SHP",
+#         "MC ETA",
+#     ]
+#     scaler_inputs_main = unpickle_file(inference_dict["final_prediction"]["scaler_inputs_path"])
+#     intermediate_model_path_dict = inference_dict["multiple_model_path"]
+
+#     y_pred, uncertainty = run_predictions(
+#         df_synth,
+#         scaler_inputs_intermediate,
+#         model_path,
+#         scaler_inputs_main.feature_names_in_,
+#         intermediate_model_path_dict,
+#         intermediate_cols,
+#     )
+#     df_synth_pred = df_synth.copy()
+#     df_synth_pred["y_pred"] = y_pred
+#     df_synth_pred["uncertainty_not_scaled"] = uncertainty
+#     min_uncertainty, max_uncertainty = (
+#         df_synth_pred["uncertainty_not_scaled"].min(),
+#         df_synth_pred["uncertainty_not_scaled"].max(),
+#     )
+#     df_synth_pred["uncertainty"] = (df_synth_pred["uncertainty_not_scaled"] - min_uncertainty) / (
+#         max_uncertainty - min_uncertainty
+#     )
+#     print("Domain space created")
+
+#     print("-----------------------------")
+#     print("Saving dataframe at", df_path)
+#     df_synth_pred.to_csv(df_path, sep=";", index=False)
+#     return df_synth_pred
 
 
 def load_domain_space(df_path):