improved tables

WTI.py

@@ -1,3 +1,4 @@
+import matplotlib as mpl
 from enum import auto
 import streamlit as st
 import pandas as pd
@@ -125,39 +126,47 @@ def displayWTI():
             AgGrid(file, key='dailyMetric', width='100%', fit_columns_on_grid_load=True,
                    enable_enterprise_modules=True, theme='streamlit', gridOptions=page)
 
-    # # # TABLES
-    # sss = pd.read_csv('WTI/CopBook1.csv')
-    # sss = sss.replace(np.nan, '', regex=True)
-    # sss.rename(columns={'Unnamed: 0': ' '}, inplace=True)
-
-    # def highlight_max(x):
-    #     return ['font-weight: bold' if v == x.loc[0] else ''
-    #             for v in x]
-    # sss = sss.style.apply(highlight_max)
-    # st.table(sss)
-
+    # TABLES
     # BRENT WTI
-    st.header("Brent vs. WTI Accuracy Metrics & Best Models")
-
-    arima = Image.open('assets/images/ARIMA2.png')
-    # st.image(arima, caption='Table of Comparisons: ARIMA',
-    #          use_column_width='auto')
-
-    col1, col2, col3 = st.columns([1, 6, 1])
-
-    with col2:
-        arima = Image.open('assets/images/ARIMA2.png')
-        st.image(arima, caption='Table of Comparisons: ARIMA',
-                 use_column_width='auto')
-        lstm = Image.open('assets/images/LSTM2.png')
-        st.image(lstm, caption='Table of Comparisons: LSTM',
-                 use_column_width='auto')
+    st.header("Brent vs. WTI Comparison")
+    st.subheader('ARIMA Accuracy Metrics & Best Models')
+    df2 = pd.DataFrame([[0.8, (0, 1, 0), 2.427, 0.017, 0.8, (0, 1, 0), 5.211, 0.023], [np.nan, np.nan, np.nan, np.nan, 0.5, (0, 1, 0), 9.498, 0.042], [0.5, (1, 0, 0), 9.366, 0.039, 0.500000, (1, 0, 0), 9.530000, 0.042000], [np.nan, np.nan, np.nan, np.nan, 0.500000, (0, 1, 0), 41.668000, 0.097000], [0.600000, (0, 1, 1), 46.308000, 0.091000, 0.600000, (0, 1, 1), 45.242000, 0.099000]], index=pd.Index(
+        ['Daily', 'Weekly*', 'Weekly', 'Monthly*', "Monthly"], name='Actual Label:'),
+        columns=(["Brent Train Split", "Brent Order", "Brent MSE", "Brent MAPE", "WTI Train Split", "WTI Order", "WTI MSE", "WTI MAPE"]))
+
+    cell_hover = {  # for row hover use <tr> instead of <td>
+        'selector': 'tr:hover',
+        'props': [('background-color', '#ff4c4c')]
+    }
+
+    index_names = {
+        'selector': '.index_name',
+        'props': 'font-style: italic; color: darkgrey; font-weight:normal;'
+    }
+    headers = {
+        'selector': 'th:not(.index_name)',
+        'props': 'background-color: #f0f2f6; color: black;'
+    }
+    df2 = df2.style
+    df2 = df2.set_table_styles(
+        [cell_hover, index_names, headers]).highlight_null(props="color: transparent;")
+    st.table(df2)
+
+    # LSTM
+    st.subheader('LSTM Accuracy Metrics & Best Models')
+    df3 = pd.DataFrame([[0.6, 4.152, 0.021, 0.8, 5.904, 0.02], [0.8, 21.62, 0.037, 0.8, 25.012, 0.039], [0.8, 56.275, 0.075, 0.8, 80.147, 0.096]], index=pd.Index(
+        ['Daily', 'Weekly', "Monthly"], name='Actual Label:'),
+        columns=(["Brent Train Split", "Brent MSE", "Brent MAPE", "WTI Train Split", "WTI MSE", "WTI MAPE"]))
+    df3 = df3.style
+    df3 = df3.set_table_styles(
+        [cell_hover, index_names, headers])
+    st.table(df3)
 
     # MODEL OUTPUT TABLE
     st.header("Model Output (Close Prices vs. Predicted Prices)")
 
     interval = st.selectbox("Select Interval:", ('Daily', 'Weekly',
-                            'Monthly'), key='bestmodels')
+                                                 'Monthly'), key='bestmodels')
 
     if interval == 'Weekly':
         file = pd.read_csv('WTI/BestWTI/bestWeekly.csv')
@@ -179,7 +188,7 @@ def displayWTI():
         # Visualization
         st.header("Visualization")
         fig = px.line(file, x=file["Date"], y=["Close Prices", "ARIMA_50.0_(0, 1, 0)_Predictions",
-                      "ARIMA_60.0_(0, 1, 1)_Predictions", "LSTM_80.0_Predictions"], title="BOTH PREDICTED WTI CRUDE OIL PRICES", width=1000)
+                                               "ARIMA_60.0_(0, 1, 1)_Predictions", "LSTM_80.0_Predictions"], title="BOTH PREDICTED WTI CRUDE OIL PRICES", width=1000)
         st.plotly_chart(fig, use_container_width=True)
 
     elif interval == 'Daily':

bakwti.py

@@ -0,0 +1,286 @@
+import matplotlib as mpl
+from enum import auto
+import streamlit as st
+import pandas as pd
+import yfinance as yf
+import matplotlib.pyplot as plt
+# import numpy as np
+import plotly.express as px
+from st_aggrid import GridOptionsBuilder, AgGrid
+import plotly.graph_objects as go
+from PIL import Image
+import numpy as np
+
+
+def displayWTI():
+    st.header("Raw Data")
+    # select time interval
+    interv = st.select_slider('Select Time Series Data Interval for Prediction', options=[
+        'Daily', 'Weekly', 'Monthly'], value='Weekly')
+    # st.write(interv[0])
+    # Function to convert time series to interval
+
+    @st.cache(persist=True, allow_output_mutation=True)
+    def getInterval(argument):
+        switcher = {
+            "W": "WTI/Weekly-WTI.csv",
+            "M": "WTI/Monthly-WTI.csv",
+            "D": "WTI/Daily-WTI.csv"
+        }
+        return switcher.get(argument, "WTI/Weekly-WTI.csv")
+
+    df = pd.read_csv(getInterval(interv[0]))
+
+    def pagination(df):
+        gb = GridOptionsBuilder.from_dataframe(df)
+        gb.configure_pagination(paginationAutoPageSize=True)
+        return gb.build()
+
+    page = pagination(df)
+    st.table(df.head())
+    # download full data
+
+    @st.cache
+    def convert_df(df):
+        # IMPORTANT: Cache the conversion to prevent computation on every rerun
+        return df.to_csv().encode('utf-8')
+
+    csv = convert_df(df)
+
+    st.download_button(
+        label="Download data as CSV",
+        data=csv,
+        file_name='WTI Oil Prices.csv',
+        mime='text/csv',
+    )
+
+    # st.header("Standard Deviation of Raw Data")
+    # sd = pd.read_csv('StandardDeviation.csv')
+    # sd.drop("Unnamed: 0", axis=1, inplace=True)
+    # # sd = sd.reset_index()
+    # AgGrid(sd, key='SD1', enable_enterprise_modules=True,
+    #        fit_columns_on_grid_load=True, theme='streamlit')
+    # st.write("Note: All entries end on 2022-06-30.")
+
+    # sd = sd.pivot(index='Start Date', columns='Interval',
+    #               values='Standard Deviation')
+    # sd = sd.reset_index()
+    # # visualization
+    # fig = px.line(sd, x=sd.index, y=['1d', '1wk', '1mo', '3mo'],
+    #               title="STANDARD DEVIATION OF BRENT CRUDE OIL PRICES", width=1000)
+    # st.plotly_chart(fig, use_container_width=True)
+
+    # accuracy metrics
+    st.header("Accuracy Metric Comparison")
+    intervals = st.selectbox(
+        "Select Interval:", ('Daily', 'Weekly', 'Monthly'), key='metricKey')
+    with st.container():
+        col1, col2 = st.columns(2)
+
+    # LSTM METRICS
+    # st.write("LSTM Metrics")
+
+    readfile = pd.read_csv('WTI/LSTM.csv')
+    # readfile = readfile[readfile['Interval'] == intervals.upper()]
+    readfile = readfile[readfile['Interval']
+                        == st.session_state.metricKey.upper()]
+    # readfile[readfile['Interval'] == intervals.upper()]
+    # readfile = updatefile(readfile)
+    readfile.drop("Unnamed: 0", axis=1, inplace=True)
+    with col1:
+        st.write("LSTM Metrics")
+        AgGrid(readfile, key=st.session_state.metricKey, fit_columns_on_grid_load=True,
+               enable_enterprise_modules=True, theme='streamlit')
+
+    # st.write(st.session_state.metricKey)
+
+    # ARIMA METRICS
+    # st.write("ARIMA Metrics")
+    # intervals = st.selectbox(
+    #     "Select Interval:", ('Weekly', 'Monthly', 'Quarterly', 'Daily'))
+
+    if intervals == 'Weekly':
+        file = pd.read_csv('WTI/ARIMAMetrics/ARIMA-WEEKLY.csv')
+        file.drop("Unnamed: 0", axis=1, inplace=True)
+        page = pagination(file)
+        with col2:
+            st.write("ARIMA Metrics")
+            AgGrid(file, width='100%', theme='streamlit', enable_enterprise_modules=True,
+                   fit_columns_on_grid_load=True, key='weeklyMetric', gridOptions=page)
+
+    elif intervals == 'Monthly':
+        file = pd.read_csv('WTI/ARIMAMetrics/ARIMA-MONTHLY.csv')
+        file.drop("Unnamed: 0", axis=1, inplace=True)
+        page = pagination(file)
+        with col2:
+            st.write("ARIMA Metrics")
+            AgGrid(file, key='monthlyMetric', fit_columns_on_grid_load=True,
+                   enable_enterprise_modules=True, theme='streamlit', gridOptions=page)
+
+    elif intervals == 'Daily':
+        file = pd.read_csv('WTI/ARIMAMetrics/ARIMA-DAILY.csv')
+        file.drop("Unnamed: 0", axis=1, inplace=True)
+        page = pagination(file)
+        with col2:
+            st.write("ARIMA Metrics")
+            AgGrid(file, key='dailyMetric', width='100%', fit_columns_on_grid_load=True,
+                   enable_enterprise_modules=True, theme='streamlit', gridOptions=page)
+
+    # TABLES
+    df2 = pd.DataFrame([[0.8, (0, 1, 0), 2.427, 0.017, 0.8, (0, 1, 0), 5.211, 0.023], [np.nan, np.nan, np.nan, np.nan, 0.5, (0, 1, 0), 9.498, 0.042], [0.5, (1, 0, 0), 9.366, 0.039, 0.500000, (1, 0, 0), 9.530000, 0.042000], [np.nan, np.nan, np.nan, np.nan, 0.500000, (0, 1, 0), 41.668000, 0.097000], [0.600000, (0, 1, 1), 46.308000, 0.091000, 0.600000, (0, 1, 1), 45.242000, 0.099000]], index=pd.Index(
+        ['Daily', 'Weekly*', 'Weekly', 'Monthly*', "Monthly"], name='Actual Label:'),
+        # columns=pd.MultiIndex.from_product([['Brent', 'WTI'], ['Train Split', 'Order', 'MSE', 'MAPE']], names=['Model:', 'Predicted:']))
+        # columns=pd.MultiIndex.from_tuples([("Brent", "Train Split"), ("Brent", "Order"), ("Brent", "MSE"), ("Brent", "MAPE"),
+        #                                    ("WTI ", "Train Split"), ("WTI", "Order"), ("WTI", "MSE"), ("WTI", "MAPE")]))
+        columns=(["Brent Train Split", "Brent Order", "Brent MSE", "Brent MAPE", "WTI Train Split", "WTI Order", "WTI MSE", "WTI MAPE"]))
+
+    # df2 = pd.DataFrame([[0.8, (0, 1, 0), 2.427, 0.017, 0.8, (0, 1, 0), 5.211, 0.023], [0.5, (1, 0, 0), 9.366, 0.039, 0.5, (0, 1, 0), 9.498, 0.042], [np.nan, np.nan, np.nan, np.nan, 0.5, (0, 1, 0), 9.498, 0.042], [0.5, (1, 0, 0), 9.366, 0.039, 0.5, (0, 1, 0), 9.498, 0.042]], index=pd.Index(
+    #     ['Daily', 'Weekly', '', 'Monthly'], name='Actual Label:'),
+    #     columns=pd.MultiIndex.from_product([['', '1'], ['Train Split', 'Order', 'MSE', 'MAPE']], names=['Model:', 'Predicted:']))
+    st.table(df2)
+    # multi_index = pd.MultiIndex.from_tuples(
+    #     [('Daily'), ('Weekly'), ('Hello World'), ('Monthly')], names=['Courses', 'Courses1', 'Courses2', 'Courses3'])
+    # col = pd.MultiIndex.from_tuples([("Brent", "Train Split"), ("Brent", "Order"), ("Brent", "MSE"), (
+    #     "Brent", "MAPE"), ("WTI ", "Train Split"), ("WTI", "Order"), ("WTI", "MSE"), ("WTI", "MAPE")])
+    # data = [[0.8, (0, 1, 0), 2.427, 0.017, 0.8, (0, 1, 0), 5.211, 0.023], [0.5, (1, 0, 0), 9.366, 0.039, 0.5, (0, 1, 0), 9.498, 0.042], [
+    #     0, 0, 0, 0, 0.5, (0, 1, 0), 9.498, 0.042], [0.5, (1, 0, 0), 9.366, 0.039, 0.5, (0, 1, 0), 9.498, 0.042]]
+    # df2 = pd.DataFrame(data, columns=col, index=multi_index)
+
+    # multi_index = pd.MultiIndex.from_tuples([("r0", "rA"),
+    #                                          ("r1", "rB")],
+    #                                         names=['Courses', 'Fee'])
+    # cols = pd.MultiIndex.from_tuples([("Gasoline", "Toyoto"),
+    #                                   ("Gasoline", "Ford"),
+    #                                   ("Electric", "Tesla"),
+    #                                   ("Electric", "Nio")])
+    # data = [[100, 300, 900, 400], [200, 500, 300, 600]]
+
+    # df2 = pd.DataFrame(data, columns=cols, index=multi_index)
+
+    cell_hover = {  # for row hover use <tr> instead of <td>
+        'selector': 'tr:hover',
+        'props': [('background-color', '#ff4c4c')]
+    }
+
+    index_names = {
+        'selector': '.index_name',
+        'props': 'font-style: italic; color: darkgrey; font-weight:normal;'
+    }
+    headers = {
+        # 'selector': 'th:not(.index_name)',
+        'selector': 'th:not(.index_name)',
+        'props': 'background-color: #f0f2f6; color: black;'
+    }
+    df2 = df2.style
+    df2 = df2.set_table_styles(
+        [cell_hover, index_names, headers]).highlight_null(props="color: transparent;")
+    df2 = df2.set_table_styles([
+        {'selector': 'th.col_heading', 'props': 'text-align: center;'},
+        {'selector': 'th.col_heading.level0', 'props': 'font-size: 1em;'},
+        {'selector': 'td', 'props': 'text-align: center; font-weight: bold;'},
+    ], overwrite=False)
+    # df2 = df2.replace(np.nan, '', regex=True)
+    st.table(df2)
+
+    # st.table(sss)
+    sss = pd.read_csv('WTI/CopBook1.csv')
+    # sss = sss.replace(np.nan, '', regex=True)
+    sss.rename(columns={'Unnamed: 0': ' '}, inplace=True)
+    sss.fillna("")
+    # sss = sss.style
+
+    # AgGrid(sss, key='WTI/CopBook1.csv', fit_columns_on_grid_load=True,
+    #    enable_enterprise_modules=True, theme='streamlit')
+    cell_hover = {  # for row hover use <tr> instead of <td>
+        'selector': 'td:hover',
+        'props': [('background-color', '#ffffb3')]
+    }
+
+    # sss = sss.style.set_properties(**{'background-color': 'black',
+    #                    'color': 'green'})
+    # sss = sss.style.set_properties(**{'background-color': 'yellow' if v ==
+    #                                sss.loc[0] else "" for v in sss}, axis=1).highlight_null(props="color: transparent;")
+
+    # sss = sss.style.apply(lambda x: ["background: red" if v ==
+    #                                  (x.iloc[1,3]) else "" for v in x], axis=1).highlight_null(props="color: transparent;")
+
+    # sss = sss.style.apply(lambda x: ["background: red"(
+    #     (x.iloc[1:3]))]).highlight_null(props="color: transparent;")
+
+    # sss.style.apply(lambda x: ["background: red" if v ==
+    #                 x.loc[0] else "" for v in x], axis=1)
+    sss = sss.style
+    sss = sss.set_table_styles(
+        [cell_hover, index_names, headers]).highlight_null(props="color: transparent;")
+    sss = sss.set_table_styles([
+        {'selector': 'th.col_heading', 'props': 'text-align: center;'},
+        {'selector': 'th.col_heading.level0', 'props': 'font-size: 1em;'},
+        {'selector': 'td', 'props': 'text-align: center; font-weight: bold;'},
+    ], overwrite=False)
+
+    # sss = sss.style.highlight_null(props="color: transparent;")
+    # sss = sss.set_table_styles([cell_hover])
+
+    # def highlight_max(x):
+    #     return ['font-weight: bold' if v == x.loc[0] else ''
+    #             for v in x]
+    # sss = sss.style.apply(highlight_max)
+
+    st.table(sss)
+
+    # BRENT WTI
+    st.header("Brent vs. WTI Accuracy Metrics & Best Models")
+
+    # arima = Image.open('assets/images/ARIMA23.png')
+    # st.image(arima, caption='Table of Comparisons: ARIMA',
+    #          use_column_width='auto')
+
+    col1, col2, col3 = st.columns([1, 6, 1])
+
+    with col2:
+        arima = Image.open('assets/images/ARIMA3111.png')
+        st.image(arima, caption='Table of Comparisons: ARIMA',
+                 use_column_width='auto')
+        lstm = Image.open('assets/images/LSTM2.png')
+        st.image(lstm, caption='Table of Comparisons: LSTM',
+                 use_column_width='auto')
+
+    # MODEL OUTPUT TABLE
+    st.header("Model Output (Close Prices vs. Predicted Prices)")
+
+    interval = st.selectbox("Select Interval:", ('Daily', 'Weekly',
+                                                 'Monthly'), key='bestmodels')
+
+    if interval == 'Weekly':
+        file = pd.read_csv('WTI/BestWTI/bestWeekly.csv')
+        page = pagination(file)
+        AgGrid(file, key='weeklycombined', fit_columns_on_grid_load=True,
+               enable_enterprise_modules=True, theme='streamlit', gridOptions=page)
+
+        # Visualization
+        st.header("Visualization")
+        fig = px.line(file, x=file["Date"], y=["Close Prices", "ARIMA_50.0_(0, 1, 0)_Predictions",
+                                               "ARIMA_50.0_(1, 0, 0)_Predictions", "LSTM_80.0_Predictions"], title="BOTH PREDICTED WTI CRUDE OIL PRICES", width=1000)
+        st.plotly_chart(fig, use_container_width=True)
+
+    elif interval == 'Monthly':
+        file = pd.read_csv('WTI/BestWTI/bestMonthly.csv')
+        page = pagination(file)
+        AgGrid(file, key='monthlyCombined', fit_columns_on_grid_load=True,
+               enable_enterprise_modules=True, theme='streamlit', gridOptions=page)
+        # Visualization
+        st.header("Visualization")
+        fig = px.line(file, x=file["Date"], y=["Close Prices", "ARIMA_50.0_(0, 1, 0)_Predictions",
+                                               "ARIMA_60.0_(0, 1, 1)_Predictions", "LSTM_80.0_Predictions"], title="BOTH PREDICTED WTI CRUDE OIL PRICES", width=1000)
+        st.plotly_chart(fig, use_container_width=True)
+
+    elif interval == 'Daily':
+        file = pd.read_csv('WTI/BestWTI/bestDaily.csv')
+        page = pagination(file)
+        AgGrid(file, key='dailyCombined', fit_columns_on_grid_load=True,
+               enable_enterprise_modules=True, theme='streamlit', gridOptions=page)
+        # Visualization
+        st.header("Visualization")
+        fig = px.line(file, x=file["Date"], y=["Close Prices", "ARIMA_80.0_(0, 1, 0)_Predictions",  # find file
+                                               "LSTM_60.0_DAILY", "LSTM_80.0_DAILY", ], title="BOTH PREDICTED WTI CRUDE OIL PRICES", width=1000)
+        st.plotly_chart(fig, use_container_width=True)