Upload folder using huggingface_hub

.env

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+IDSC_ACC=sentient_test@idsc.com.sg
+IDSC_PASS=APItest1412

data/multivariate/blow_mold_future.csv

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+datetime,gas_regm,global_price_of_rubber
+2023-01-31,3.7405,73.1689
+2023-02-28,3.7726,74.5348
+2023-03-31,3.8121,75.9514

data/multivariate/blow_mold_historical.csv

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+2018-03-31,83.0,2.591,66.02,252.0,78.1670180931732,238.1
+2018-04-30,83.0,2.757,72.11,248.9,77.7415047447978,239.7
+2018-05-31,83.0,2.901,76.98,254.3,76.1236900344774,236.2
+2018-06-30,83.0,2.891,74.41,257.4,70.0483530041459,235.7
+2018-07-31,83.0,2.849,74.25,257.7,66.1977615602277,234.2
+2018-08-31,83.0,2.836,72.53,262.7,66.5917590376227,234.2
+2018-09-30,86.0,2.836,78.89,260.3,64.9477007375421,230.7
+2018-10-31,86.0,2.86,81.03,260.8,64.4318867137904,233.7
+2018-11-30,83.0,2.647,64.75,254.6,61.0147262177998,233.7
+2018-12-31,80.0,2.366,57.36,248.7,64.4388411130883,224.9
+2019-01-31,80.0,2.248,59.41,242.7,71.8944761455489,223.8
+2019-02-28,80.0,2.309,63.96,241.3,73.9009577214054,223.7
+2019-03-31,80.0,2.516,66.14,239.7,77.7544645422103,223.7
+2019-04-30,83.0,2.798,71.23,239.2,77.6248665680858,224.4
+2019-05-31,83.0,2.859,71.32,241.7,81.5516851840572,221.4
+2019-06-30,83.0,2.716,64.22,238.2,89.9164386367575,222.7
+2019-07-31,80.0,2.74,63.92,237.2,79.7238841910249,222.0
+2019-08-31,77.0,2.621,59.04,237.1,67.7304932369297,220.9
+2019-09-30,80.0,2.592,62.83,237.6,68.1361348959393,220.9
+2019-10-31,80.0,2.627,59.71,238.7,65.121509293294,221.1
+2019-11-30,77.0,2.598,63.21,235.4,69.723710078964,219.7
+2019-12-31,77.0,2.555,67.31,229.5,74.949424390598,217.7
+2020-01-31,47.0,2.548,63.65,228.9,75.2125082780706,215.5
+2020-02-29,47.0,2.442,55.66,232.8,71.6608757971895,218.0
+2020-03-31,47.0,2.234,32.01,231.9,68.3729457395668,217.4
+2020-04-30,43.0,1.841,18.38,216.6,60.6993711474302,219.1
+2020-05-31,43.0,1.87,29.38,210.2,61.37364060725,214.1
+2020-06-30,47.0,2.082,40.27,212.1,64.7854381831213,211.7
+2020-07-31,52.0,2.183,43.24,217.3,67.9849372594608,216.5
+2020-08-31,57.0,2.182,44.74,225.1,79.9616260398618,214.3
+2020-09-30,62.0,2.183,40.91,226.0,89.3124438678774,220.9
+2020-10-31,62.0,2.158,40.19,238.2,101.520278489552,224.0
+2020-11-30,62.0,2.108,42.69,240.0,110.151798107092,222.4
+2020-12-31,67.0,2.195,49.99,246.3,107.745595720848,228.4
+2021-01-31,72.0,2.334,54.77,256.7,104.398944035707,229.2
+2021-02-28,79.0,2.501,62.28,271.7,105.246154792108,233.0
+2021-03-31,86.0,2.81,65.41,302.3,108.160215326471,242.9
+2021-04-30,93.0,2.858,64.81,321.8,99.3282073014635,247.4
+2021-05-31,98.0,2.985,68.53,329.8,105.414992152843,255.9
+2021-06-30,103.0,3.064,73.16,342.7,97.0688826192269,268.6
+2021-07-31,108.0,3.136,75.17,353.336,84.9233924775288,275.777
+2021-08-31,108.0,3.158,70.75,358.454,86.2496117460692,282.857
+2021-09-30,108.0,3.175,74.49,358.01,81.2240575782592,286.721
+2021-10-31,103.0,3.291,83.54,359.606,85.5368228883846,285.666
+2021-11-30,98.0,3.395,81.05,353.75,88.5024565295995,296.322
+2021-12-31,93.0,3.307,74.17,343.395,88.1867087380963,292.442
+2022-01-31,93.0,3.315,86.51,328.684,90.21055782856,285.039
+2022-02-28,93.0,3.517,97.13,328.986,96.8899654160606,284.688
+2022-03-31,97.0,4.222,117.25,328.095,98.3014720470411,288.808
+2022-04-30,97.0,4.109,104.58,343.327,97.0667226529915,291.62
+2022-05-31,100.0,4.444,113.34,349.319,96.0544839308527,291.348
+2022-06-30,100.0,4.929,122.71,352.112,94.0215629822002,304.147
+2022-07-31,97.0,4.559,111.93,342.621,82.938383507189,304.969
+2022-08-31,93.0,3.975,100.45,329.278,73.3045584509684,303.837
+2022-09-30,90.0,3.7,89.76,326.451,66.9564987905564,308.976
+2022-10-31,90.0,3.815,93.33,316.901,68.5357286494898,312.788
+2022-11-30,90.0,3.685,91.42,300.185,65.2658343099326,307.226
+2022-12-31,90.0,3.21,80.92,291.825,65.2658343099326,307.226

demo.py

@@ -1,4 +1,5 @@
 import json
+import pandas as pd
 
 import gradio as gr
 from gr_app import args, GradioApp
@@ -113,7 +114,7 @@ with demo:
     # Forecasting #
     # =========== #
 
-    column__models = gr.Column(visible=False)
+    column__models = gr.Column(visible=True)
 
     with column__models:
         md__fit_ready = gr.Markdown(**args.md__fit_ready)
@@ -192,7 +193,6 @@ with demo:
         # Prophet #
         # ------- #
         with gr.Tab('Prophet'):
-            gr.Markdown('Prophet')
 
             btn__forecast_with_prophet = gr.Button(
                 **args.btn__forecast_with_prophet)
@@ -211,6 +211,30 @@ with demo:
              df__prophet_result]
         )
 
+        # ---------- #
+        # Auto ARIMA #
+        # ---------- #
+        with gr.Tab('Auto ARIMA'):
+
+            btn__auto_arima_forecast = gr.Button(
+                value='Forecast with auto ARIMA', variant='primary')
+
+            plot__auto_arima_result = gr.Plot()
+
+            with gr.Row():
+                df__auto_arima_result = gr.DataFrame()
+                file__auto_arima_result = gr.File()
+
+        btn__auto_arima_forecast.click(
+            app.btn__auto_arima_forecast__click,
+            [],
+            [
+                plot__auto_arima_result,
+                df__auto_arima_result,
+                file__auto_arima_result
+            ]
+        )
+
     # --------- #
     # Operators #
     # --------- #

gr_app/__init__.py

@@ -1 +1 @@
-from .gr_app import GradioApp
+from .gr_app import GradioApp

gr_app/args.py

@@ -74,6 +74,12 @@ btn__forecast_with_prophet = {
     'variant': 'primary'
 }
 
+btn__forecast_with_auto_arima = {
+    'value': 'Forecast with auto ARIMA',
+    'variant': 'primary'
+}
+
+
 json_xgboost_params = {
     'elem_classes': 'json_xgboost_params'
 }

gr_app/components/__init__.py

@@ -0,0 +1 @@
+from .auto_arima import AutoARIMAForecaster

gr_app/components/auto_arima.py

@@ -0,0 +1,85 @@
+import gradio as gr
+from sktime.forecasting.arima import AutoARIMA
+from sktime.utils.plotting import plot_series
+import matplotlib.pyplot as plt
+
+
+class AutoARIMAForecaster():
+    def __init__(self) -> None:
+
+        print('[Auto ARIMA] init model')
+
+    def cta(self):
+        return
+
+    def fit_predict(
+            self,
+            y_train,
+            y_test,
+            y_future,
+            fh,
+            fh_test,
+            sp,
+            round_results=False,
+            d=None,
+            D=None,
+            seasonal=True,
+            X=None,
+            X_train=None,
+            X_test=None,
+            X_future=None
+    ):
+        print('[Auto ARIMA] Start forecasting')
+        self.round_decimal = 0 if round_results else 4
+        forecaster = AutoARIMA(
+            sp=sp,
+            d=d,
+            D=D,
+            seasonal=seasonal,
+            random=True,
+            n_fits=5,
+            error_action='ignore'
+        )
+
+        self.y_train = y_train
+        self.y_test = y_test
+        self.y_future = y_future
+        self.sp = sp
+
+        forecaster.fit(y_train, X_train)
+
+        print('[Auto ARIMA] - Fitted complete')
+
+        self.test = forecaster.predict(fh_test, X_test)
+        self.predict_interval = forecaster.predict_interval(
+            fh_test, X_test, coverage=.9)
+
+        forecaster.update(y_future, X, update_params=False)
+
+        self.forecast = forecaster.predict(fh, X_future)
+
+        self.forecast_interval = forecaster.predict_interval(
+            fh, X_future, coverage=.9)
+
+        self.test = round(self.test, self.round_decimal)
+        self.forecast = round(self.forecast, self.round_decimal)
+
+        print('[Auto ARIMA] Forecast completed')
+
+    def plot_results(self, figsize=(12, 6)):
+        fig, ax = plt.subplots(figsize=figsize)
+        plot_series(
+            self.y_train[-2*self.sp:],
+            self.y_test,
+            self.test,
+            self.forecast,
+            labels=["y_train (part)", "y_test", "y_pred", 'y_forecast'],
+            x_label='Date',
+            pred_interval=self.forecast_interval,
+            ax=ax)
+
+        ax.set_title('Auto ARIMA Forecast Result')
+        ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
+        ax.legend(loc='upper left')
+        # fig.tight_layout()
+        return fig

gr_app/gr_app.py

@@ -20,6 +20,11 @@ from src.idsc import IDSC
 
 from src.forecaster.models import ProphetForecaster
 
+'''
+Below is a new structure, still experimenting the possibility
+'''
+from .components import AutoARIMAForecaster
+
 
 class GradioApp():
     def __init__(
@@ -29,13 +34,13 @@ class GradioApp():
         self.analyser = Analyser()
         self.idsc = IDSC()
 
-        self.historical_demo_data = 'data/multivariate/demo_historical.csv'
-        self.future_demo_data = 'data/multivariate/demo_future.csv'
+        self.historical_demo_data = 'data/multivariate/blow_mold_historical.csv'
+        self.future_demo_data = 'data/multivariate/blow_mold_future.csv'
 
         self.data: pd.DataFrame = None
         self.n_predict = 3
-        self.window_length = 7
-        self.target_column = 'y'
+        self.window_length = 2
+        self.target_column = 'price'
         self.exog_columns = []
 
         # Define if the model's result is going to be rounded
@@ -67,6 +72,8 @@ class GradioApp():
         self.prophet__weekly_seasonality = False
         self.prophet__daily_seasonality = False
 
+        self.auto_arima = AutoARIMAForecaster()
+
     def checkbox__round_results__change(self, val):
         self.round_results = val
 
@@ -75,7 +82,9 @@ class GradioApp():
         self.target_column = val
 
     def btn__profiling__click(self):
-        self.analyser.fit(self.data)
+        self.analyser.fit(
+            self.data,
+            target_col=self.target_column)
         self.analyser.profiling()
 
         return (
@@ -257,7 +266,7 @@ class GradioApp():
             self.forecaster.fh_test,
             self.forecaster.period,
             self.forecaster.freq,
-            X=self.forecaster.exog,
+            X=self.forecaster.X,
             seasonality_mode=self.prophet__seasonality_mode,
             add_country_holidays=self.prophet__add_country_holidays,
             yearly_seasonality=self.prophet__yearly_seasonality,
@@ -283,6 +292,7 @@ class GradioApp():
             ax=ax)
 
         ax.set_title('Prophet Forecast Result')
+        ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
         ax.legend(loc='upper left')
         fig.tight_layout()
 
@@ -297,6 +307,46 @@ class GradioApp():
         prophet_forecast_df = self.prophet.forecast.reset_index()
         return gr.Dataframe(value=prophet_forecast_df)
 
+    # -------------------------------- #
+    # Auto ARIMA Operations & Updaters #
+    # -------------------------------- #
+    def btn__auto_arima_forecast__click(self):
+        self.auto_arima.fit_predict(
+            y_train=self.forecaster.y_train,
+            y_test=self.forecaster.y_test,
+            y_future=self.forecaster.y,
+            fh=self.forecaster.fh,
+            fh_test=self.forecaster.fh_test,
+            sp=self.forecaster.period,
+            round_results=self.round_results,
+            d=1,
+            D=0,
+            seasonal=True,
+            X=self.forecaster.X,
+            X_train=self.forecaster.X_train,
+            X_test=self.forecaster.X_test,
+            X_future=self.forecaster.X_future)
+        return (
+            self.update__plot__auto_arima_result(),
+            self.update__df__auto_arima_result(),
+            self.update__file__auto_arima_result()
+        )
+
+    def update__plot__auto_arima_result(self):
+        fig = self.auto_arima.plot_results(
+            figsize=self.plot_figsize_full_screen)
+        return gr.Plot(fig)
+
+    def update__df__auto_arima_result(self):
+        auto_arima_result_df = self.auto_arima.forecast.reset_index()
+        return gr.Dataframe(auto_arima_result_df)
+
+    def update__file__auto_arima_result(self):
+        auto_arima_result_df = self.auto_arima.forecast
+        auto_arima_result_path = self.__create_temp_csv_file(
+            auto_arima_result_df)
+        return gr.File(auto_arima_result_path)
+
     # =============================== #
     # || Gradio Component Updaters || #
     # =============================== #
@@ -304,7 +354,7 @@ class GradioApp():
     def update__plot__changepoints(self):
         fig, axs = plt.subplots(2, 1, figsize=(20, 8))
 
-        axs[0].plot(self.data[['y']])
+        axs[0].plot(self.data[[self.target_column]])
 
         axs[0].text(self.data.index[0],
                     axs[0].get_ylim()[1]*0.9,
@@ -318,7 +368,7 @@ class GradioApp():
                         self.analyser.quantity_predictability[i+1],
                         fontsize=20)
 
-        axs[1].plot(self.data[['y']])
+        axs[1].plot(self.data[[self.target_column]])
 
         axs[1].text(self.data.index[0],
                     axs[1].get_ylim()[1]*0.9,

src/analyser/analyser.py

@@ -14,8 +14,13 @@ class Analyser():
         self.idsc = IDSC()
         self.viz = Visualiser()
 
-    def fit(self, data):
+    def fit(
+        self, 
+        data,
+        target_col: str = None
+        ):
         self.data = data
+        self.target_col = target_col
 
     def profiling(self):
         # --------------------------------- #
@@ -26,7 +31,7 @@ class Analyser():
         self.characteristic = None
 
         self.profile = self.idsc.profiling(
-            self.data[['y']].rename(columns={'y': 'target'}).dropna().to_json())
+            self.data[[self.target_col]].rename(columns={self.target_col: 'target'}).dropna().to_json())
         # Parse profile result
         self.intermittent_change_points_idx = self.profile['change_point_res']['inter_order_cpi']
         self.quantity_change_points_idx = self.profile['change_point_res']['order_quantity_cpi']

src/forecaster/forecaster.py

@@ -8,7 +8,7 @@ from sktime.forecasting.model_selection import temporal_train_test_split
 from typing import List
 import logging
 
-from .utils import split_x_y, k_folds
+from .utils import split_x_y
 from .models import AllModels
 
 
@@ -69,7 +69,8 @@ class Forecaster():
             self.data,
             window_length,
             n_predict,
-            self.freq)
+            self.freq,
+            target_col=self.target_col)
 
         if exog is not None:
             print('[Forecaster - fit] - exogenous data provided')

src/forecaster/models/prophet.py

@@ -1,4 +1,5 @@
 from sktime.forecasting.fbprophet import Prophet
+import pandas as pd
 
 
 class ProphetForecaster():
@@ -15,17 +16,20 @@ class ProphetForecaster():
             sp,
             freq,
             round_val=False,
-            X=None,
             seasonality_mode=None,
             add_country_holidays=None,
             yearly_seasonality=False,
             weekly_seasonality=False,
-            daily_seasonality=False
+            daily_seasonality=False,
+            X: pd.DataFrame = None,
+            X_train=None,
+            X_test=None,
+            X_future: pd.DataFrame = None
     ):
 
         print('[Prophet] Start forecasting')
 
-        round_decimal = 0 if round else 0.4
+        round_decimal = 0 if round_val else 4
 
         forecaster = Prophet(
             seasonality_mode=seasonality_mode,
@@ -36,16 +40,17 @@ class ProphetForecaster():
             daily_seasonality=daily_seasonality
         )
 
-        forecaster.fit(y_train)
+        forecaster.fit(y_train, X_train)
 
-        self.predict = forecaster.predict(fh_test)
+        self.predict = forecaster.predict(fh_test, X_test)
         self.predict_interval = forecaster.predict_interval(
-            fh_test, coverage=.9)
+            fh_test, X_test,  coverage=.9)
 
-        forecaster.update(y_future, update_params=False)
+        forecaster.update(y_future, X, update_params=False)
 
-        self.forecast = forecaster.predict(fh)
-        self.forecast_interval = forecaster.predict_interval(fh, coverage=.9)
+        self.forecast = forecaster.predict(fh, X_future)
+        self.forecast_interval = forecaster.predict_interval(
+            fh, X_future, coverage=.9)
 
         self.predict = round(self.predict, round_decimal)
         self.predict_interval = round(self.predict_interval, round_decimal)

src/forecaster/utils/prep_data.py

@@ -9,10 +9,11 @@ def split_x_y(
     window_length: int,
     n_predict: int,
     freq: str,
+    target_col: str = 'y'
 ):
     # print('[prep_data] ----- Start -----')
     datetime_index = data.index
-    y = data['y']
+    y = data[target_col]
     X_train, X_forecast = None, None
 
     has_X = len(data.columns) > 1
@@ -20,7 +21,7 @@ def split_x_y(
     if has_X:
         # print('[prep_data] - additional feature columns found')
 
-        X = data.drop(columns='y').reset_index(drop=True)
+        X = data.drop(columns=target_col).reset_index(drop=True)
         X_columns = X.columns
 
         X_train = pd.DataFrame()
@@ -75,7 +76,8 @@ def k_folds(
         period: int,
         window_length: int,
         n_predict: int,
-        freq: str
+        freq: str,
+        target_col: str = 'y'
 ):
     '''
     Amount of folds for testing is data size - window length and 2 seasonality period
@@ -101,7 +103,8 @@ def k_folds(
                 d,
                 window_length,
                 n_predict,
-                freq
+                freq,
+                target_col=target_col
             ))
 
     print('[k_folds] ----- END -----')

src/idsc/IDSC.py

@@ -13,7 +13,7 @@ from .auto_arima import auto_arima
 
 
 from dotenv import load_dotenv
-# load_dotenv()
+load_dotenv()
 
 
 class IDSC():

src/idsc/config.yml

@@ -1,2 +1,2 @@
-apikey: dff418c479f0c77b4e1162271831ebaabafce35b
-apikey_expire: 12/14/2023, 20:57:06
+apikey: 51a8043a7bb7db880b04d9b35962a2429e646c61
+apikey_expire: 12/21/2023, 15:39:52

temp/20231218165125.csv

@@ -0,0 +1,4 @@
+,price
+2023-01-31,87.0
+2023-02-28,87.0
+2023-03-31,87.0

temp/20231218165600.csv

@@ -0,0 +1,4 @@
+,price
+2023-01-31,73.0
+2023-02-28,78.0
+2023-03-31,80.0

temp/20231218165649.csv

@@ -0,0 +1,4 @@
+,price
+2023-01-31,73.0
+2023-02-28,78.0
+2023-03-31,80.0

temp/20231218165805.csv

@@ -0,0 +1,4 @@
+,price
+2023-01-31,73.0
+2023-02-28,78.0
+2023-03-31,80.0