df_j_tmp = df_tmp_2 # + # df_i # + if using_SOAP: df_to_use = df_j_tmp else: df_to_use = df_i df_j = simplify_df_features_targets( df_to_use, # df_i, # df_j_tmp, target_ads="o", # feature_ads="o", feature_ads="o", ) df_format = df_features_targets[( "format", "color", "stoich", )] # - # ### Removing columns with no variance # + df_j_info = df_j.describe()
new_cols.append(("features", "o", col_i)) df_SOAP_i.columns = pd.MultiIndex.from_tuples(new_cols) df_tmp = df_i df_tmp_2 = pd.concat([ df_tmp, df_SOAP_i, ], axis=1) df_j_tmp = df_tmp_2 # + df_j = simplify_df_features_targets( df_j_tmp, target_ads="o", feature_ads="o", ) df_format = df_features_targets[( "format", "color", "stoich", )] # - # ### Removing columns with no variance # + df_j_info = df_j.describe()
# y_array = df_comb["targets"]["g_oh"].tolist() # trace = go.Scatter( # x=x_array, # y=y_array, # mode="markers", # ) # data = [trace] # fig = go.Figure(data=data) # fig.show() # + df_j = simplify_df_features_targets( df_comb, target_ads=target_ads_i, feature_ads=feature_ads_i, ) df_format = df_features_targets[( "format", "color", "stoich", )] # - # ### Creating pre-DFT features # + # pre_dft_features = True pre_dft_features = False