| import streamlit as st |
| import pandas as pd |
| import numpy as np |
| import sklearn |
| import time |
| import plotly.express as px |
| from Univariate import Univariate_Models |
| from Multivariate import Multivariate_Models |
|
|
| st.set_page_config(layout="wide") |
|
|
|
|
| def GetFile(): |
| uploaded_file = st.file_uploader("", type=["csv", 'json']) |
| file_type = st.selectbox("Please select file type", ['csv', 'json']) |
| if uploaded_file is not None: |
| uploaded_file.seek(0) |
| if file_type == 'json': |
| data = pd.read_json(uploaded_file) |
| return data |
| elif file_type == 'csv': |
| seperator = st.text_input("Input csv seperator here ", ",") |
| data = pd.read_csv(uploaded_file, sep=seperator) |
| |
| return data |
|
|
| def header(): |
| col1, col2, col3, col4, col5 = st.columns(5) |
| with col1: st.image('./assets/yash_logo.png') |
| with col2: st.write(' ') |
| with col3: st.write(' ') |
| with col4: st.write(' ') |
| with col5: st.write(' ') |
| st.markdown("<h1 style='text-align: center;'>Time Series Accelerator - AutoML</h1>", |
| unsafe_allow_html=True) |
|
|
|
|
| def univaiate_modelling(data, date_column, target_column): |
| |
| |
| data[date_column] = pd.to_datetime(data[date_column]) |
| data = data.set_index(date_column) |
| data = data[[target_column]] |
| |
| forcasting_horizon = round(data.shape[0] * 0.067) |
| st.write("Forecasting horizon selected as: ", forcasting_horizon) |
|
|
| |
| with st.spinner('Initializing modeling setup...'): |
| uni_mod = Univariate_Models(data, forcasting_horizon) |
| |
| with st.spinner('Getting Stats...'): |
| st.write("Statistics of the Data") |
| st.dataframe(uni_mod.get_stats()) |
| |
|
|
| |
| with st.spinner('Creating Plots...'): |
| stats_plots = uni_mod.get_plots() |
|
|
| for name, plt in stats_plots.items(): |
| st.write(name) |
| st.plotly_chart(plt) |
|
|
| |
| with st.spinner('Training Multiple models'): |
| model_metric_df, best_mod_param_df, best_model = uni_mod.train_models() |
| |
|
|
| st.write("Models trained and their metrics") |
| st.write(model_metric_df) |
|
|
| st.write("Parameters of Best Model") |
| st.dataframe(best_mod_param_df) |
|
|
| |
| st.write("Predicted values") |
| st.dataframe(uni_mod.get_predictions()) |
|
|
| insam, outsam = uni_mod.plot_insample_and_forecast() |
| st.plotly_chart(insam) |
| st.plotly_chart(outsam) |
|
|
|
|
| def multivaiate_modelling(data, target_column, date_column, time_interval): |
| forcasting_horizon = round(data.shape[0] * 0.067) |
| st.write("Forecasting horizon selected as: ", forcasting_horizon) |
|
|
| |
| with st.spinner('Initializing modeling setup...'): |
| multi_mod = Multivariate_Models(data, forcasting_horizon, target_column, date_column, |
| time_interval) |
| |
| with st.spinner('Getting Stats...'): |
| st.write("Statistics of the Data") |
| cat_features, num_features, desc_df = multi_mod.get_stats() |
| st.dataframe(desc_df) |
| st.write("Inferred numerical columns :", ", ".join(num_features)) |
| st.write("Inferred categorical columns :", ", ".join(cat_features)) |
|
|
| |
| with st.spinner('Training Multiple models...'): |
| model_metric_df, best_mod_param_df, _ = multi_mod.train_models() |
| |
| st.write("Models trained and their metrics") |
| st.write(model_metric_df) |
|
|
| st.write("Parameters of Best Model") |
| st.dataframe(best_mod_param_df) |
|
|
| |
| with st.spinner('Creating Plots...'): |
| stats_plots = multi_mod.get_plots() |
|
|
| for name, plt in stats_plots.items(): |
| try: |
| st.write(name) |
| |
| st.image(plt) |
| except Exception as e: |
| print(f"[PLOT EXCEPTION] ==> {type(e).__name__} : {e}") |
|
|
| |
| st.write("Predicted values") |
| st.dataframe(multi_mod.get_predictions()) |
|
|
| insam, outsam = multi_mod.plot_insample_and_forecast() |
| st.plotly_chart(insam) |
| st.plotly_chart(outsam) |
|
|
|
|
| def main(): |
| data = GetFile() |
| |
|
|
| if (data is not None): |
| data_shape = data.shape |
| st.write("Data head", data.head()) |
| st.write("Data shape :", data_shape) |
|
|
| ts_type_radio = st.radio("Please select the type of analysis required: ", |
| ('Univariate', 'Multivariate')) |
|
|
| all_cols = set(data.columns) |
| target_column = st.selectbox('Select the dependent variable', all_cols) |
| date_column = st.selectbox('Select the date column', |
| all_cols - set([target_column])) |
| time_interval_radio = st.radio("Please select the type of analysis required: ", |
| ('daily', 'weekly', 'monthly', 'yearly'), index=0) |
| |
| ts_type = ts_type_radio.lower().strip() |
| start_btn = st.button('Start Modelling') |
|
|
| if start_btn and ts_type == "univariate": |
| univaiate_modelling(data, date_column, target_column) |
|
|
| elif start_btn and ts_type == "multivariate": |
| multivaiate_modelling(data, target_column, date_column, time_interval_radio) |
|
|
|
|
|
|
| if __name__=='__main__': |
| header() |
| main() |
|
|
