no idea

docker/Dockerfile

 # syntax = docker/dockerfile:1.3
-FROM nvidia/cuda:11.6.2-base-ubuntu20.04
+FROM pytorch/pytorch:2.1.2-cuda11.8-cudnn8-runtime
 RUN apt update && apt install -y \
-    pip \
+    git \
     && rm -rf /var/lib/apt/lists/*
-COPY requirements.txt /tmp/requirements.txt
+COPY ../requirements.txt /tmp/requirements.txt
 RUN pip install --upgrade pip && \
     pip install --no-cache-dir -r /tmp/requirements.txt
-WORKDIR /home/wavo-torch/notebooks
-CMD jupyter-lab --no-browser --ip 0.0.0.0 --port 8888 --allow-root
-#--notebook-dir=/tf 
-LABEL name="kiwavo-tk" version="0.0.4" maintainer="Michel Spils <msp@informatik.uni-kiel.de>"
-# docker run -u $(id -u):$(id -g) --gpus all --rm -it -p 9999:9999 --name wln --workdir /../app/ki-wavo/notebooks 
-# --mount type=bind,source="$(pwd)",target=/app  kiwavo/notebook:latest jupyter-lab --ip 0.0.0.0 --port 9999
-#pytorch-forecasting==1.0.0
+ARG USERNAME=mspils
+ARG USER_UID=5006
+ARG USER_GID=$USER_UID
+# Create the user
+RUN groupadd --gid $USER_GID $USERNAME \
+    && useradd --uid $USER_UID --gid $USER_GID -m $USERNAME
+LABEL name="wavo-dev" version="0.0.2" maintainer="Michel Spils <msp@informatik.uni-kiel.de>"
\ No newline at end of file

docker/Dockerfile_devcontainer → docker/Dockerfile_old

 # syntax = docker/dockerfile:1.3
-FROM pytorch/pytorch:2.1.2-cuda11.8-cudnn8-runtime
+FROM nvidia/cuda:11.6.2-base-ubuntu20.04
 RUN apt update && apt install -y \
-    git \
+    pip \
     && rm -rf /var/lib/apt/lists/*
 COPY requirements.txt /tmp/requirements.txt
 RUN pip install --upgrade pip && \
     pip install --no-cache-dir -r /tmp/requirements.txt
-ARG USERNAME=mspils
-ARG USER_UID=5006
-ARG USER_GID=$USER_UID
-# Create the user
-RUN groupadd --gid $USER_GID $USERNAME \
-    && useradd --uid $USER_UID --gid $USER_GID -m $USERNAME
-LABEL name="wavo-dev" version="0.0.2" maintainer="Michel Spils <msp@informatik.uni-kiel.de>"
\ No newline at end of file
+WORKDIR /home/wavo-torch/notebooks
+CMD jupyter-lab --no-browser --ip 0.0.0.0 --port 8888 --allow-root
+#--notebook-dir=/tf 
+LABEL name="kiwavo-tk" version="0.0.4" maintainer="Michel Spils <msp@informatik.uni-kiel.de>"
+# docker run -u $(id -u):$(id -g) --gpus all --rm -it -p 9999:9999 --name wln --workdir /../app/ki-wavo/notebooks 
+# --mount type=bind,source="$(pwd)",target=/app  kiwavo/notebook:latest jupyter-lab --ip 0.0.0.0 --port 9999
+#pytorch-forecasting==1.0.0

src/main_hyper.py

@@ -140,11 +140,11 @@ class Objective:
 
     def _get_model_params(self,trial : optuna.Trial):
         #differencing = 0
-        differencing = trial.suggest_int("differencing", 0, 1)
+        differencing = trial.suggest_int("differencing", 1, 1)
         learning_rate = trial.suggest_float("lr", 0.00001, 0.01)
         # optimizer = trial.suggest_categorical("optimizer", ["adam","adamw"])  
         optimizer = trial.suggest_categorical("optimizer", ["adam"])
-        embed_time = trial.suggest_categorical("embed_time", [True,False])
+        embed_time = trial.suggest_categorical("embed_time", [False])
         #model_architecture = trial.suggest_categorical("model_architecture", ["classic_lstm"])
         #model_architecture = trial.suggest_categorical("model_architecture", ["transformer","autoformer"]) #TODO add models here
         #model_architecture = trial.suggest_categorical("model_architecture", ["autoformer"])
@@ -174,7 +174,7 @@ class Objective:
         if model_architecture in ["classic_lstm", "last_lstm"]:
             model_params = dict(
                 hidden_size_lstm=trial.suggest_int("hidden_size_lstm", 32, 512),
-                num_layers_lstm=trial.suggest_int("n_layers_lstm", 1, 3),
+                num_layers_lstm=trial.suggest_int("n_layers_lstm", 1, 2),#3
                 # num_layers_lstm = trial.suggest_int("n_layers_lstm", 1, 1),
                 hidden_size=trial.suggest_int("hidden_size", 32, 512),
                 num_layers=trial.suggest_int("n_layers", 2, 4),

src/utils/utility.py

@@ -8,7 +8,7 @@ import torch
 import pandas as pd
 from utils.timefeatures import time_features
 
-def fill_missing_values(df: pd.DataFrame, max_fill=10) -> pd.DataFrame:
+def fill_missing_values(df: pd.DataFrame, max_fill=200) -> pd.DataFrame:
     """
     Fills values in a DataFrame.
     First columns recognized as Precipitation columns (containing NEW or NVh) are filled with 0,
@@ -26,7 +26,7 @@ def fill_missing_values(df: pd.DataFrame, max_fill=10) -> pd.DataFrame:
     na_count = df.isna().sum(axis=0)
 
     # get all columns with precipitation and fill missing values with 0
-    mask = df.columns.str.contains('NEW') | df.columns.str.contains('NVh')
+    mask = df.columns.str.contains('NEW') | df.columns.str.contains('NVh') | (df.columns.str.startswith('N') & df.columns.str.endswith('_mm'))
 
     prec_cols = list(na_count[mask][na_count[mask] > 0].index)
     if len(prec_cols) > 0: