bugfix

.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,116 @@
+## Imports
+import pickle
+import warnings
+import streamlit as st
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime
+
+# import torch
+from torch.distributions import Normal
+from pytorch_forecasting import (
+    TimeSeriesDataSet,
+    TemporalFusionTransformer,
+)
+
+## Functions 
+def raw_preds_to_df(raw,quantiles = None):
+    """
+    raw is output of model.predict with return_index=True
+    quantiles can be provided like [0.1,0.5,0.9] to get interpretable quantiles
+    in the output, time_idx is the first prediction time index (one step after knowledge cutoff)
+    pred_idx the index of the predicted date i.e. time_idx + h - 1
+    """
+    index = raw[2]
+    preds = raw[0].prediction
+    dec_len = preds.shape[1]
+    n_quantiles = preds.shape[-1]
+    preds_df = pd.DataFrame(index.values.repeat(dec_len * n_quantiles, axis=0),columns=index.columns)
+    preds_df = preds_df.assign(h=np.tile(np.repeat(np.arange(1,1+dec_len),n_quantiles),len(preds_df)//(dec_len*n_quantiles)))
+    preds_df = preds_df.assign(q=np.tile(np.arange(n_quantiles),len(preds_df)//n_quantiles))
+    preds_df = preds_df.assign(pred=preds.flatten().cpu().numpy())
+    if quantiles is not None:
+        preds_df['q'] = preds_df['q'].map({i:q for i,q in enumerate(quantiles)})
+
+    preds_df['pred_idx'] = preds_df['time_idx'] + preds_df['h'] - 1
+    return preds_df
+
+def prepare_dataset(parameters, df, rain, temperature, datepicker):
+    if rain != "Default":
+        df["MTXWTH_Day_precip"] = rain_mapping[rain]
+    
+    df["MTXWTH_Temp_min"] = df["MTXWTH_Temp_min"] + temperature
+    df["MTXWTH_Temp_max"] = df["MTXWTH_Temp_max"] + temperature
+
+    lowerbound = datepicker - datetime.timedelta(days = 35) 
+    upperbound = datepicker + datetime.timedelta(days = 30) 
+
+    df = df.loc[(df["Date"]>lowerbound) & (df["Date"]<=upperbound)]
+    
+    df = TimeSeriesDataSet.from_parameters(parameters, df)
+    return df.to_dataloader(train=False, batch_size=256,num_workers = 0)
+
+def predict(model, dataloader): 
+    return model.predict(dataloader, mode="raw", return_x=True, return_index=True)
+    
+## Initiate Data
+with open('data/parameters.pkl', 'rb') as f:
+    parameters = pickle.load(f)
+model = TemporalFusionTransformer.load_from_checkpoint('model/tft_check.ckpt')
+
+df = pd.read_pickle('data/test_data.pkl')
+df = df.loc[(df["Branch"] == 15) & (df["Group"].isin(["6","7","4","1"]))]
+
+rain_mapping = {
+    "Yes" : 1,
+    "No" : 0
+}
+
+# Start App
+st.title("Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting")
+
+st.markdown(body = """
+    ### Abstract
+    Multi-horizon forecasting often contains a complex mix of inputs – including
+    static (i.e. time-invariant) covariates, known future inputs, and other exogenous
+    time series that are only observed in the past – without any prior information
+    on how they interact with the target. Several deep learning methods have been
+    proposed, but they are typically ‘black-box’ models which do not shed light on
+    how they use the full range of inputs present in practical scenarios. In this pa-
+    per, we introduce the Temporal Fusion Transformer (TFT) – a novel attention-
+    based architecture which combines high-performance multi-horizon forecasting
+    with interpretable insights into temporal dynamics. To learn temporal rela-
+    tionships at different scales, TFT uses recurrent layers for local processing and
+    interpretable self-attention layers for long-term dependencies. TFT utilizes spe-
+    cialized components to select relevant features and a series of gating layers to
+    suppress unnecessary components, enabling high performance in a wide range of
+    scenarios. On a variety of real-world datasets, we demonstrate significant per-
+    formance improvements over existing benchmarks, and showcase three practical
+    interpretability use cases of TFT.
+""")
+
+rain = st.radio("Rain Indicator", ('Default', 'Yes', 'No'))
+
+temperature = st.slider('Change in Temperature', min_value=-10, max_value=+10, value=0, step=0.25)
+
+datepicker = st.date_input("Start of Forecast", datetime.date(2022, 12, 24), min_value=datetime.date(2022, 6, 26) + datetime.timedelta(days = 35), max_value=datetime.date(2023, 6, 26) - datetime.timedelta(days = 30))
+
+arr = np.random.normal(1, 1, size=100)
+fig, ax = plt.subplots()
+ax.hist(arr, bins=20)
+
+st.pyplot(fig)
+
+st.button("Forecast Sales", type="primary") #on_click=None,
+
+# %%
+preds = raw_preds_to_df(out, quantiles = None)
+
+preds = preds.merge(data_selected[['time_idx','Group','Branch','sales','weight','Date','MTXWTH_Day_precip','MTXWTH_Temp_max','MTXWTH_Temp_min']],how='left',left_on=['pred_idx','Group','Branch'],right_on=['time_idx','Group','Branch'])
+preds.rename(columns={'time_idx_x':'time_idx'},inplace=True)
+preds.drop(columns=['time_idx_y'],inplace=True)
+
+

app.py

@@ -66,7 +66,7 @@ df = df.loc[(df["Branch"] == 15) & (df["Group"].isin(["6","7","4","1"]))]
 
 rain_mapping = {
     "Yes" : 1,
-    "No" : , 0
+    "No" : 0
 }
 
 # Start App