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import streamlit as st
from util import developer_info, developer_info_static
from src.plot import correlation_matrix_plotly, plot_residuals, plot_predictions_vs_actual, plot_qq_plot
from src.handle_null_value import contains_missing_value, remove_high_null, fill_null_values
from src.preprocess import convert_to_numeric, remove_rows_with_empty_target, remove_duplicates, transform_data_for_clustering
from src.llm_service import decide_fill_null, decide_encode_type, decide_target_attribute, decide_test_ratio, decide_regression_model
from src.pca import decide_pca, perform_PCA_for_regression
from src.model_service import split_data, save_model, calculate_r2_score, calculate_mse_and_rmse, calculate_mae
from src.regression_model import train_selected_regression_model
from src.util import select_Y, contain_null_attributes_info, separate_fill_null_list, check_all_columns_numeric, non_numeric_columns_and_head, separate_decode_list, get_data_overview, attribute_info, get_regression_method_name
def start_training_model():
st.session_state["start_training"] = True
def regression_model_pipeline(DF, API_KEY, GPT_MODEL):
st.divider()
st.subheader('Data Overview')
if 'data_origin' not in st.session_state:
st.session_state.data_origin = DF
st.dataframe(st.session_state.data_origin.describe(), width=1200)
attributes = st.session_state.data_origin.columns.tolist()
# Select the target variable
if 'target_selected' not in st.session_state:
st.session_state.target_selected = False
st.subheader('Target Variable')
if not st.session_state.target_selected:
with st.spinner("AI is analyzing the data..."):
attributes_for_target, types_info_for_target, head_info_for_target = attribute_info(st.session_state.data_origin)
st.session_state.target_Y = decide_target_attribute(attributes_for_target, types_info_for_target, head_info_for_target, GPT_MODEL, API_KEY)
if st.session_state.target_Y != -1:
selected_Y = st.session_state.target_Y
st.success("Target variable has been selected by the AI!")
st.write(f'Target attribute selected: :green[**{selected_Y}**]')
st.session_state.target_selected = True
else:
st.info("AI cannot determine the target variable from the data. Please select the target variable")
target_col1, target_col2 = st.columns([9, 1])
with target_col1:
selected_Y = st.selectbox(
label = 'Select the target variable to predict:',
options = attributes,
index = len(attributes)-1,
label_visibility='collapsed'
)
with target_col2:
if st.button("Confirm", type="primary"):
st.session_state.target_selected = True
st.session_state.selected_Y = selected_Y
else:
if st.session_state.target_Y != -1:
st.success("Target variable has been selected by the AI!")
st.write(f"Target variable selected: :green[**{st.session_state.selected_Y}**]")
if st.session_state.target_selected:
# Data Imputation
st.subheader('Handle and Impute Missing Values')
if "contain_null" not in st.session_state:
st.session_state.contain_null = contains_missing_value(st.session_state.data_origin)
if 'filled_df' not in st.session_state:
if st.session_state.contain_null:
with st.status("Processing **missing values** in the data...", expanded=True) as status:
st.write("Filtering out high-frequency missing rows and columns...")
filled_df = remove_high_null(DF)
filled_df = remove_rows_with_empty_target(filled_df, st.session_state.selected_Y)
st.write("Large language model analysis...")
attributes, types_info, description_info = contain_null_attributes_info(filled_df)
fill_result_dict = decide_fill_null(attributes, types_info, description_info, GPT_MODEL, API_KEY)
st.write("Imputing missing values...")
mean_list, median_list, mode_list, new_category_list, interpolation_list = separate_fill_null_list(fill_result_dict)
filled_df = fill_null_values(filled_df, mean_list, median_list, mode_list, new_category_list, interpolation_list)
# Store the imputed DataFrame in session_state
st.session_state.filled_df = filled_df
DF = filled_df
status.update(label='Missing value processing completed!', state="complete", expanded=False)
st.download_button(
label="Download Data with Missing Values Imputed",
data=st.session_state.filled_df.to_csv(index=False).encode('utf-8'),
file_name="imputed_missing_values.csv",
mime='text/csv')
else:
st.session_state.filled_df = DF
st.success("No missing values detected. Processing skipped.")
else:
st.success("Missing value processing completed!")
if st.session_state.contain_null:
st.download_button(
label="Download Data with Missing Values Imputed",
data=st.session_state.filled_df.to_csv(index=False).encode('utf-8'),
file_name="imputed_missing_values.csv",
mime='text/csv')
# Data Encoding
st.subheader("Process Data Encoding")
st.caption("*For considerations of processing time, **NLP features** like **TF-IDF** have not been included in the current pipeline, long text attributes may be dropped.")
if 'all_numeric' not in st.session_state:
st.session_state.all_numeric = check_all_columns_numeric(st.session_state.data_origin)
if 'encoded_df' not in st.session_state:
if not st.session_state.all_numeric:
with st.status("Encoding non-numeric data using **numeric mapping** and **one-hot**...", expanded=True) as status:
non_numeric_attributes, non_numeric_head = non_numeric_columns_and_head(DF)
st.write("Large language model analysis...")
encode_result_dict = decide_encode_type(non_numeric_attributes, non_numeric_head, GPT_MODEL, API_KEY)
st.write("Encoding the data...")
convert_int_cols, one_hot_cols, drop_cols = separate_decode_list(encode_result_dict, st.session_state.selected_Y)
encoded_df, mappings = convert_to_numeric(DF, convert_int_cols, one_hot_cols, drop_cols)
# Store the imputed DataFrame in session_state
st.session_state.encoded_df = encoded_df
DF = encoded_df
status.update(label='Data encoding completed!', state="complete", expanded=False)
st.download_button(
label="Download Encoded Data",
data=st.session_state.encoded_df.to_csv(index=False).encode('utf-8'),
file_name="encoded_data.csv",
mime='text/csv')
else:
st.session_state.encoded_df = DF
st.success("All columns are numeric. Processing skipped.")
else:
st.success("Data encoded completed using numeric mapping and one-hot!")
if not st.session_state.all_numeric:
st.download_button(
label="Download Encoded Data",
data=st.session_state.encoded_df.to_csv(index=False).encode('utf-8'),
file_name="encoded_data.csv",
mime='text/csv')
# Correlation Heatmap
if 'df_cleaned1' not in st.session_state:
st.session_state.df_cleaned1 = DF
st.subheader('Correlation Between Attributes')
st.plotly_chart(correlation_matrix_plotly(st.session_state.df_cleaned1))
# Remove duplicate entities
st.subheader('Remove Duplicate Entities')
if 'df_cleaned2' not in st.session_state:
st.session_state.df_cleaned2 = remove_duplicates(st.session_state.df_cleaned1)
# DF = remove_duplicates(DF)
st.info("Duplicate rows removed.")
# Data Transformation
st.subheader('Data Transformation')
if 'data_transformed' not in st.session_state:
st.session_state.data_transformed = transform_data_for_clustering(st.session_state.df_cleaned2)
st.success("Data transformed by standardization and box-cox if applicable.")
# PCA
st.subheader('Principal Component Analysis')
st.write("Deciding whether to perform PCA...")
if 'df_pca' not in st.session_state:
_, n_components = decide_pca(st.session_state.df_cleaned2)
st.session_state.df_pca = perform_PCA_for_regression(st.session_state.data_transformed, n_components, st.session_state.selected_Y)
st.success("Completed!")
if "start_training" not in st.session_state:
st.session_state["start_training"] = False
# AI decide the testing set percentage
if 'test_percentage' not in st.session_state:
with st.spinner("Deciding testing set percentage based on data..."):
st.session_state.test_percentage = int(decide_test_ratio(st.session_state.df_pca.shape, GPT_MODEL, API_KEY) * 100)
splitting_column, balance_column = st.columns(2)
with splitting_column:
st.subheader('Data Splitting')
st.caption('AI recommended test percentage for the model')
st.slider('Percentage of test set', 1, 25, st.session_state.test_percentage, key='test_percentage', disabled=st.session_state['start_training'])
with balance_column:
st.metric(label="Test Data", value=f"{st.session_state.test_percentage}%", delta=None)
st.toggle('Class Balancing', value=False, key='to_perform_balance', disabled=True)
st.caption('Class balancing is not applicable to regression models.')
st.button("Start Training Model", on_click=start_training_model, type="primary", disabled=st.session_state['start_training'])
# Model Training
if st.session_state['start_training']:
with st.container():
st.header("Modeling")
X_train_res, Y_train_res = select_Y(st.session_state.df_pca, st.session_state.selected_Y)
# Splitting the data
if not st.session_state.get("data_splitted", False):
st.session_state.X_train, st.session_state.X_test, st.session_state.Y_train, st.session_state.Y_test = split_data(X_train_res, Y_train_res, st.session_state.test_percentage / 100, 42, True)
st.session_state["data_splitted"] = True
# Decide model types:
if "decided_model" not in st.session_state:
st.session_state["decided_model"] = False
if "all_set" not in st.session_state:
st.session_state["all_set"] = False
if not st.session_state["decided_model"]:
with st.spinner("Deciding models based on data..."):
shape_info, _, _, description_info = get_data_overview(st.session_state.df_pca)
model_dict = decide_regression_model(shape_info, description_info, st.session_state.selected_Y, GPT_MODEL, API_KEY)
model_list = list(model_dict.values())
if 'model_list' not in st.session_state:
st.session_state.model_list = model_list
st.session_state["decided_model"] = True
# Show modeling results
if st.session_state["decided_model"]:
display_results(st.session_state.X_train, st.session_state.X_test, st.session_state.Y_train, st.session_state.Y_test)
st.session_state["all_set"] = True
# Download models
if st.session_state["all_set"]:
download_col1, download_col2, download_col3 = st.columns(3)
with download_col1:
st.download_button(label="Download Model", data=st.session_state.downloadable_model1, file_name=f"{st.session_state.model1_name}.joblib", mime="application/octet-stream")
with download_col2:
st.download_button(label="Download Model", data=st.session_state.downloadable_model2, file_name=f"{st.session_state.model2_name}.joblib", mime="application/octet-stream")
with download_col3:
st.download_button(label="Download Model", data=st.session_state.downloadable_model3, file_name=f"{st.session_state.model3_name}.joblib", mime="application/octet-stream")
# Footer
st.divider()
if "all_set" in st.session_state and st.session_state["all_set"]:
if "has_been_set" not in st.session_state:
st.session_state["has_been_set"] = True
developer_info()
else:
developer_info_static()
def display_results(X_train, X_test, Y_train, Y_test):
st.success("Models selected based on your data!")
# Data set metrics
data_col1, data_col2, data_col3 = st.columns(3)
with data_col1:
st.metric(label="Total Data", value=len(X_train)+len(X_test), delta=None)
with data_col2:
st.metric(label="Training Data", value=len(X_train), delta=None)
with data_col3:
st.metric(label="Testing Data", value=len(X_test), delta=None)
# Model training
model_col1, model_col2, model_col3 = st.columns(3)
with model_col1:
if "model1_name" not in st.session_state:
st.session_state.model1_name = get_regression_method_name(st.session_state.model_list[0])
st.subheader(st.session_state.model1_name)
with st.spinner("Model training in progress..."):
if 'model1' not in st.session_state:
st.session_state.model1 = train_selected_regression_model(X_train, Y_train, st.session_state.model_list[0])
st.session_state.y_pred1 = st.session_state.model1.predict(X_test)
st.session_state.downloadable_model1 = save_model(st.session_state.model1)
# Model metrics
st.write("R2 Score: ", f':green[**{calculate_r2_score(st.session_state.y_pred1, Y_test)}**]')
st.pyplot(plot_predictions_vs_actual(st.session_state.y_pred1, Y_test))
mse1, rmse1 = calculate_mse_and_rmse(st.session_state.y_pred1, Y_test)
st.write("Mean Squared Error: ", f':green[**{mse1}**]')
st.write("Root Mean Squared Error: ", f':green[**{rmse1}**]')
st.pyplot(plot_residuals(st.session_state.y_pred1, Y_test))
st.write("Mean Absolute Error: ", f':green[**{calculate_mae(st.session_state.y_pred1, Y_test)}**]')
st.pyplot(plot_qq_plot(st.session_state.y_pred1, Y_test))
with model_col2:
if "model2_name" not in st.session_state:
st.session_state.model2_name = get_regression_method_name(st.session_state.model_list[1])
st.subheader(st.session_state.model2_name)
with st.spinner("Model training in progress..."):
if 'model2' not in st.session_state:
st.session_state.model2 = train_selected_regression_model(X_train, Y_train, st.session_state.model_list[1])
st.session_state.y_pred = st.session_state.model2.predict(X_test)
st.session_state.downloadable_model2 = save_model(st.session_state.model2)
# Model metrics
st.write("R2 Score: ", f':green[**{calculate_r2_score(st.session_state.y_pred, Y_test)}**]')
st.pyplot(plot_predictions_vs_actual(st.session_state.y_pred, Y_test))
mse2, rmse2 = calculate_mse_and_rmse(st.session_state.y_pred, Y_test)
st.write("Mean Squared Error: ", f':green[**{mse2}**]')
st.write("Root Mean Squared Error: ", f':green[**{rmse2}**]')
st.pyplot(plot_residuals(st.session_state.y_pred, Y_test))
st.write("Mean Absolute Error: ", f':green[**{calculate_mae(st.session_state.y_pred, Y_test)}**]')
st.pyplot(plot_qq_plot(st.session_state.y_pred, Y_test))
with model_col3:
if "model3_name" not in st.session_state:
st.session_state.model3_name = get_regression_method_name(st.session_state.model_list[2])
st.subheader(st.session_state.model3_name)
with st.spinner("Model training in progress..."):
if 'model3' not in st.session_state:
st.session_state.model3 = train_selected_regression_model(X_train, Y_train, st.session_state.model_list[2])
st.session_state.y_pred3 = st.session_state.model3.predict(X_test)
st.session_state.downloadable_model3 = save_model(st.session_state.model3)
# Model metrics
st.write("R2 Score: ", f':green[**{calculate_r2_score(st.session_state.y_pred3, Y_test)}**]')
st.pyplot(plot_predictions_vs_actual(st.session_state.y_pred3, Y_test))
mse3, rmse3 = calculate_mse_and_rmse(st.session_state.y_pred3, Y_test)
st.write("Mean Squared Error: ", f':green[**{mse3}**]')
st.write("Root Mean Squared Error: ", f':green[**{rmse3}**]')
st.pyplot(plot_residuals(st.session_state.y_pred3, Y_test))
st.write("Mean Absolute Error: ", f':green[**{calculate_mae(st.session_state.y_pred3, Y_test)}**]')
st.pyplot(plot_qq_plot(st.session_state.y_pred3, Y_test)) |