added app.py

app.py

@@ -0,0 +1,30 @@
+from get_forecast import get_forecast
+import gradio as gr
+
+with gr.Blocks() as demo:
+
+    gr.Markdown(
+    """
+    # AB InBev Data Science Challenge
+    This is a demo built for AB InBev company to forecast **SOM** and **Volume** series. This demo allows the user to analyze the price sensibility of the mentioned series. Enjoy!
+    ## How do you use this demo?
+    Using this demo is easy! Simply follow the next steps:
+    1. Select a Serie to forecast from the dropdown (valid values: SOM and Volumen)
+    2. Choose how many months ahead do you wish your forecast to predict
+    3. Pick the price variation vs last year you want to analyze. Setting this value to 0 means the future price values are exactly the same as the last year. 
+    """)
+
+    with gr.Row():
+        serie = gr.Dropdown(choices = ['SOM', 'Volumen'], value = 'SOM', label = 'Serie', info = 'Choose the serie to forecast')
+        periods = gr.Slider(minimum = 1, maximum = 12, step = 1, value = 3, label = 'Months')
+        percent_change = gr.Slider(minimum = -100, maximum = 100, step = 5, value = -5, label = '% Change vs Last Year')
+
+    plot = gr.Plot()
+
+    serie.change(get_forecast, [serie, periods, percent_change], plot, queue=False)
+    periods.change(get_forecast, [serie, periods, percent_change], plot, queue=False)
+    percent_change.change(get_forecast, [serie, periods, percent_change], plot, queue=False)    
+    plot.change(get_forecast, [serie, periods, percent_change], plot, queue=False)    
+    demo.load(get_forecast, [serie, periods, percent_change], plot, queue=False)
+
+    demo.launch(debug = False)

data/som_data.csv

@@ -0,0 +1,38 @@
+ds;y;precio_hl
+2019-09-01;0.020915653261496473;7401.807657174472
+2019-10-01;0.020581492528706422;7690.883661312737
+2019-11-01;0.020928808454534462;7543.184995957615
+2019-12-01;0.018754425666550188;7310.693659862146
+2020-01-01;0.02302447244897762;7238.999879400494
+2020-02-01;0.022806337555348007;7384.595116461657
+2020-03-01;0.022806150221354564;7606.022219882967
+2020-04-01;0.024818567088805782;6641.800669106801
+2020-05-01;0.0274311269204994;6541.786707784563
+2020-06-01;0.026539351889114132;6670.899239709285
+2020-07-01;0.034426844435406806;6880.257062804495
+2020-08-01;0.034388557604764836;6859.994191302926
+2020-09-01;0.03214360739132953;6650.605461375102
+2020-10-01;0.030103086698821023;7109.852838006651
+2020-11-01;0.02655257622967919;7113.3090297969875
+2020-12-01;0.02734047812743239;7197.972073541302
+2021-01-01;0.026089987008928173;6374.009481095359
+2021-02-01;0.025612409955957597;7011.995289620221
+2021-03-01;0.02240233628534395;7282.817007379381
+2021-04-01;0.024883998075455216;7897.530131607511
+2021-05-01;0.02376304605512934;7500.698636152339
+2021-06-01;0.02368671149006748;7954.630801419836
+2021-07-01;0.020318662312505783;7258.247057883863
+2021-08-01;0.01968912735303638;7534.222846892518
+2021-09-01;0.01945239533267099;6536.559231045296
+2021-10-01;0.023854422999473247;7027.180461980301
+2021-11-01;0.02308125053238265;7523.51279634985
+2021-12-01;0.02004340977554878;7459.715436564322
+2022-01-01;0.020192252849173826;7050.156843690086
+2022-02-01;0.021809051071164234;6949.758834627189
+2022-03-01;0.0230171214550591;7032.027423804479
+2022-04-01;0.02648682876822832;7692.4848674508
+2022-05-01;0.02349760546286519;7462.713414643129
+2022-06-01;0.023459162166123263;7658.313830664824
+2022-07-01;0.023312392516184378;7667.218777734914
+2022-08-01;0.027115976192388886;7143.871781123152
+2022-09-01;0.028064184905544503;7530.285881422287

data/volumen_data.csv

@@ -0,0 +1,38 @@
+ds;y;precio_hl
+2019-09-01;2158.4562666666666;7401.807657174472
+2019-10-01;2150.84358;7690.883661312737
+2019-11-01;1605.3423333333335;7543.184995957615
+2019-12-01;1728.7750285714285;7310.693659862146
+2020-01-01;2948.445853846154;7238.999879400494
+2020-02-01;2742.9243846153845;7384.595116461657
+2020-03-01;2622.698146153846;7606.022219882967
+2020-04-01;1036.4133250000002;6641.800669106801
+2020-05-01;1110.5237666666667;6541.786707784563
+2020-06-01;1220.0236666666667;6670.899239709285
+2020-07-01;1731.5539777777776;6880.257062804495
+2020-08-01;2012.7762555555555;6859.994191302926
+2020-09-01;2566.6432800000002;6650.605461375102
+2020-10-01;2838.76117;7109.852838006651
+2020-11-01;2925.1163727272724;7113.3090297969875
+2020-12-01;3206.5506090909093;7197.972073541302
+2021-01-01;3183.832609090909;6374.009481095359
+2021-02-01;3562.052745454545;7011.995289620221
+2021-03-01;3242.5764750000003;7282.817007379381
+2021-04-01;3452.8987454545454;7897.530131607511
+2021-05-01;3260.101309090909;7500.698636152339
+2021-06-01;2785.2703090909094;7954.630801419836
+2021-07-01;2385.9406833333337;7258.247057883863
+2021-08-01;2321.3464;7534.222846892518
+2021-09-01;2294.8570999999997;6536.559231045296
+2021-10-01;3017.342372727273;7027.180461980301
+2021-11-01;2935.186718181818;7523.51279634985
+2021-12-01;2575.5009750000004;7459.715436564322
+2022-01-01;2873.6602461538464;7050.156843690086
+2022-02-01;2754.920866666667;6949.758834627189
+2022-03-01;3379.66115;7032.027423804479
+2022-04-01;3286.3523099999998;7692.4848674508
+2022-05-01;2847.9890818181816;7462.713414643129
+2022-06-01;2899.5531545454546;7658.313830664824
+2022-07-01;3286.186963636363;7667.218777734914
+2022-08-01;3657.71656;7143.871781123152
+2022-09-01;3827.83348;7530.285881422287

get_forecast.py

@@ -0,0 +1,54 @@
+import pandas as pd
+from dateutil.relativedelta import relativedelta
+import matplotlib.pyplot as plt
+from prophet.serialize import model_from_json
+
+def get_forecast(serie: str, periods, percent_change: int):
+    
+    if serie == 'SOM':
+        model_file, data_file = 'som_model.json', 'som_data.csv'    
+    elif serie == 'Volumen':
+        model_file, data_file = 'volumen_model.json', 'volumen_data.csv'
+    else:
+        raise ValueError('Input not valid')
+    
+    # load model files
+    with open(model_file, 'r') as fin:
+        model = model_from_json(fin.read())  
+    history = pd.read_csv(data_file, encoding = 'utf-8-sig', sep = ';')
+    history['ds'] = pd.to_datetime(history['ds'])
+        
+    # make future dataframe
+    future = model.make_future_dataframe(periods = periods, freq = 'MS')
+    future = future.tail(periods)
+
+    # filter prices last year
+    last_year_dates = future.ds.apply(lambda x: x - relativedelta(years=1))
+    past_prices = history[history['ds'].isin(last_year_dates)]['precio_hl'].values
+
+    # generate new_prices
+    
+    percent_change = percent_change / 100
+    
+    new_prices = past_prices * (1 + percent_change)
+
+    future['precio_hl'] = new_prices
+
+    forecast = model.predict(future)
+
+    future_values = forecast[['ds', 'yhat']]
+
+    last_obs = history.iloc[-1:][['ds', 'y']].rename(columns = {'y': 'yhat'})
+    future_aux = pd.concat([last_obs, future_values])
+
+    fig = plt.figure()
+    #plt.plot(history['ds'], history['y'], label = 'Historic data', marker = '.', color = 'C0')
+    plt.plot(future_aux['ds'], future_aux['yhat'], label = 'Forecast', marker = '.', color = 'C1')
+    plt.plot(history['ds'], history['y'], label = 'Historic data', marker = '.', color = 'C0')
+    plt.xticks(rotation = 45)
+    #plt.xlabel('Date')
+    plt.ylabel(serie)
+    plt.tight_layout()
+    plt.legend()
+    
+    return fig

models/som_model.json

@@ -0,0 +1 @@
+{"growth": "linear", "n_changepoints": 25, "specified_changepoints": false, "changepoint_range": 0.8, "yearly_seasonality": "auto", "weekly_seasonality": "auto", "daily_seasonality": "auto", "seasonality_mode": "additive", "seasonality_prior_scale": 10.0, "changepoint_prior_scale": 0.05, "holidays_prior_scale": 10.0, "mcmc_samples": 0, "interval_width": 0.8, "uncertainty_samples": 1000, "y_scale": 0.034426844435406806, "logistic_floor": false, "country_holidays": null, "component_modes": {"additive": ["yearly", "precio_hl", "additive_terms", "extra_regressors_additive", "holidays"], "multiplicative": ["multiplicative_terms", "extra_regressors_multiplicative"]}, "changepoints": "{\"name\":\"ds\",\"index\":[1,2,3,4,6,7,8,9,10,11,12,13,15,16,17,18,19,20,21,22,24,25,26,27,28],\"data\":[\"2019-10-01T00:00:00.000\",\"2019-11-01T00:00:00.000\",\"2019-12-01T00:00:00.000\",\"2020-01-01T00:00:00.000\",\"2020-03-01T00:00:00.000\",\"2020-04-01T00:00:00.000\",\"2020-05-01T00:00:00.000\",\"2020-06-01T00:00:00.000\",\"2020-07-01T00:00:00.000\",\"2020-08-01T00:00:00.000\",\"2020-09-01T00:00:00.000\",\"2020-10-01T00:00:00.000\",\"2020-12-01T00:00:00.000\",\"2021-01-01T00:00:00.000\",\"2021-02-01T00:00:00.000\",\"2021-03-01T00:00:00.000\",\"2021-04-01T00:00:00.000\",\"2021-05-01T00:00:00.000\",\"2021-06-01T00:00:00.000\",\"2021-07-01T00:00:00.000\",\"2021-09-01T00:00:00.000\",\"2021-10-01T00:00:00.000\",\"2021-11-01T00:00:00.000\",\"2021-12-01T00:00:00.000\",\"2022-01-01T00:00:00.000\"]}", "history_dates": "{\"name\":\"ds\",\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],\"data\":[\"2019-09-01T00:00:00.000\",\"2019-10-01T00:00:00.000\",\"2019-11-01T00:00:00.000\",\"2019-12-01T00:00:00.000\",\"2020-01-01T00:00:00.000\",\"2020-02-01T00:00:00.000\",\"2020-03-01T00:00:00.000\",\"2020-04-01T00:00:00.000\",\"2020-05-01T00:00:00.000\",\"2020-06-01T00:00:00.000\",\"2020-07-01T00:00:00.000\",\"2020-08-01T00:00:00.000\",\"2020-09-01T00:00:00.000\",\"2020-10-01T00:00:00.000\",\"2020-11-01T00:00:00.000\",\"2020-12-01T00:00:00.000\",\"2021-01-01T00:00:00.000\",\"2021-02-01T00:00:00.000\",\"2021-03-01T00:00:00.000\",\"2021-04-01T00:00:00.000\",\"2021-05-01T00:00:00.000\",\"2021-06-01T00:00:00.000\",\"2021-07-01T00:00:00.000\",\"2021-08-01T00:00:00.000\",\"2021-09-01T00:00:00.000\",\"2021-10-01T00:00:00.000\",\"2021-11-01T00:00:00.000\",\"2021-12-01T00:00:00.000\",\"2022-01-01T00:00:00.000\",\"2022-02-01T00:00:00.000\",\"2022-03-01T00:00:00.000\",\"2022-04-01T00:00:00.000\",\"2022-05-01T00:00:00.000\",\"2022-06-01T00:00:00.000\",\"2022-07-01T00:00:00.000\",\"2022-08-01T00:00:00.000\",\"2022-09-01T00:00:00.000\"]}", "train_holiday_names": null, "start": 1567296000.0, "t_scale": 94694400.0, "holidays": null, "history": "{\"schema\":{\"fields\":[{\"name\":\"ds\",\"type\":\"datetime\"},{\"name\":\"y\",\"type\":\"number\"},{\"name\":\"precio_hl\",\"type\":\"number\"},{\"name\":\"floor\",\"type\":\"integer\"},{\"name\":\"t\",\"type\":\"number\"},{\"name\":\"y_scaled\",\"type\":\"number\"}],\"pandas_version\":\"1.4.0\"},\"data\":[{\"ds\":\"2019-09-01T00:00:00.000\",\"y\":0.0209156533,\"precio_hl\":0.4383736274,\"floor\":0,\"t\":0.0,\"y_scaled\":0.6075390761},{\"ds\":\"2019-10-01T00:00:00.000\",\"y\":0.0205814925,\"precio_hl\":1.162364969,\"floor\":0,\"t\":0.0273722628,\"y_scaled\":0.5978326758},{\"ds\":\"2019-11-01T00:00:00.000\",\"y\":0.0209288085,\"precio_hl\":0.7924534116,\"floor\":0,\"t\":0.0556569343,\"y_scaled\":0.6079211963},{\"ds\":\"2019-12-01T00:00:00.000\",\"y\":0.0187544257,\"precio_hl\":0.2101784669,\"floor\":0,\"t\":0.0830291971,\"y_scaled\":0.5447616816},{\"ds\":\"2020-01-01T00:00:00.000\",\"y\":0.0230244724,\"precio_hl\":0.0306212724,\"floor\":0,\"t\":0.1113138686,\"y_scaled\":0.6687941584},{\"ds\":\"2020-02-01T00:00:00.000\",\"y\":0.0228063376,\"precio_hl\":0.39526479,\"floor\":0,\"t\":0.1395985401,\"y_scaled\":0.6624579722},{\"ds\":\"2020-03-01T00:00:00.000\",\"y\":0.0228061502,\"precio_hl\":0.9498293453,\"floor\":0,\"t\":0.1660583942,\"y_scaled\":0.6624525307},{\"ds\":\"2020-04-01T00:00:00.000\",\"y\":0.0248185671,\"precio_hl\":-1.4650651604,\"floor\":0,\"t\":0.1943430657,\"y_scaled\":0.7209074051},{\"ds\":\"2020-05-01T00:00:00.000\",\"y\":0.0274311269,\"precio_hl\":-1.7155502958,\"floor\":0,\"t\":0.2217153285,\"y_scaled\":0.7967946924},{\"ds\":\"2020-06-01T00:00:00.000\",\"y\":0.0265393519,\"precio_hl\":-1.3921877411,\"floor\":0,\"t\":0.25,\"y_scaled\":0.7708912137},{\"ds\":\"2020-07-01T00:00:00.000\",\"y\":0.0344268444,\"precio_hl\":-0.8678507189,\"floor\":0,\"t\":0.2773722628,\"y_scaled\":1.0},{\"ds\":\"2020-08-01T00:00:00.000\",\"y\":0.0343885576,\"precio_hl\":-0.9185991148,\"floor\":0,\"t\":0.3056569343,\"y_scaled\":0.9988878786},{\"ds\":\"2020-09-01T00:00:00.000\",\"y\":0.0321436074,\"precio_hl\":-1.4430135433,\"floor\":0,\"t\":0.3339416058,\"y_scaled\":0.9336785848},{\"ds\":\"2020-10-01T00:00:00.000\",\"y\":0.0301030867,\"precio_hl\":-0.2928277113,\"floor\":0,\"t\":0.3613138686,\"y_scaled\":0.8744073758},{\"ds\":\"2020-11-01T00:00:00.000\",\"y\":0.0265525762,\"precio_hl\":-0.2841716731,\"floor\":0,\"t\":0.3895985401,\"y_scaled\":0.7712753424},{\"ds\":\"2020-12-01T00:00:00.000\",\"y\":0.0273404781,\"precio_hl\":-0.0721329368,\"floor\":0,\"t\":0.4169708029,\"y_scaled\":0.7941616078},{\"ds\":\"2021-01-01T00:00:00.000\",\"y\":0.026089987,\"precio_hl\":-2.135748644,\"floor\":0,\"t\":0.4452554745,\"y_scaled\":0.757838467},{\"ds\":\"2021-02-01T00:00:00.000\",\"y\":0.02561241,\"precio_hl\":-0.5379121069,\"floor\":0,\"t\":0.473540146,\"y_scaled\":0.7439662384},{\"ds\":\"2021-03-01T00:00:00.000\",\"y\":0.0224023363,\"precio_hl\":0.1403613436,\"floor\":0,\"t\":0.4990875912,\"y_scaled\":0.6507229069},{\"ds\":\"2021-04-01T00:00:00.000\",\"y\":0.0248839981,\"precio_hl\":1.6799114036,\"floor\":0,\"t\":0.5273722628,\"y_scaled\":0.7228079856},{\"ds\":\"2021-05-01T00:00:00.000\",\"y\":0.0237630461,\"precio_hl\":0.6860462516,\"floor\":0,\"t\":0.5547445255,\"y_scaled\":0.6902475799},{\"ds\":\"2021-06-01T00:00:00.000\",\"y\":0.0236867115,\"precio_hl\":1.8229201277,\"floor\":0,\"t\":0.5830291971,\"y_scaled\":0.6880302821},{\"ds\":\"2021-07-01T00:00:00.000\",\"y\":0.0203186623,\"precio_hl\":0.0788258635,\"floor\":0,\"t\":0.6104014599,\"y_scaled\":0.5901982202},{\"ds\":\"2021-08-01T00:00:00.000\",\"y\":0.0196891274,\"precio_hl\":0.7700076941,\"floor\":0,\"t\":0.6386861314,\"y_scaled\":0.5719120551},{\"ds\":\"2021-09-01T00:00:00.000\",\"y\":0.0194523953,\"precio_hl\":-1.7286425202,\"floor\":0,\"t\":0.6669708029,\"y_scaled\":0.5650356764},{\"ds\":\"2021-10-01T00:00:00.000\",\"y\":0.023854423,\"precio_hl\":-0.499880817,\"floor\":0,\"t\":0.6943430657,\"y_scaled\":0.6929018152},{\"ds\":\"2021-11-01T00:00:00.000\",\"y\":0.0230812505,\"precio_hl\":0.7431843544,\"floor\":0,\"t\":0.7226277372,\"y_scaled\":0.670443397},{\"ds\":\"2021-12-01T00:00:00.000\",\"y\":0.0200434098,\"precio_hl\":0.5834037589,\"floor\":0,\"t\":0.75,\"y_scaled\":0.5822029322},{\"ds\":\"2022-01-01T00:00:00.000\",\"y\":0.0201922528,\"precio_hl\":-0.4423364301,\"floor\":0,\"t\":0.7782846715,\"y_scaled\":0.5865263918},{\"ds\":\"2022-02-01T00:00:00.000\",\"y\":0.0218090511,\"precio_hl\":-0.6937834137,\"floor\":0,\"t\":0.8065693431,\"y_scaled\":0.6334896918},{\"ds\":\"2022-03-01T00:00:00.000\",\"y\":0.0230171215,\"precio_hl\":-0.4877415929,\"floor\":0,\"t\":0.8321167883,\"y_scaled\":0.6685806333},{\"ds\":\"2022-04-01T00:00:00.000\",\"y\":0.0264868288,\"precio_hl\":1.1663751925,\"floor\":0,\"t\":0.8604014599,\"y_scaled\":0.7693655693},{\"ds\":\"2022-05-01T00:00:00.000\",\"y\":0.0234976055,\"precio_hl\":0.5909122,\"floor\":0,\"t\":0.8877737226,\"y_scaled\":0.6825373004},{\"ds\":\"2022-06-01T00:00:00.000\",\"y\":0.0234591622,\"precio_hl\":1.080793773,\"floor\":0,\"t\":0.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models/volumen_model.json

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