test new nn

run3.py

@@ -0,0 +1,56 @@
+import pandas as pd
+
+import tensorflow.keras
+
+import numpy as np
+import pandas as pd
+import xgboost as xg
+
+import tensorflow.keras
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+
+in_columns = ["id_stacji", "nazwa_stacji", "typ_zbioru", "rok", "miesiąc"]
+df = pd.read_csv("train/in.tsv", names=in_columns, sep="\t")
+df_test = pd.read_csv("dev-0/in.tsv", names=in_columns, sep="\t")
+
+df = pd.concat([df, df_test])
+x = pd.get_dummies(df, columns=["id_stacji", "rok", "miesiąc"])
+x = x.drop(["nazwa_stacji", "typ_zbioru"], axis=1)
+x = x.iloc[:-600]
+y = pd.read_csv("train/expected.tsv", sep="\t", names=["rainfall"])
+
+model = Sequential(
+    [
+        Dense(1024, activation="relu", input_dim=73),
+        Dense(512, activation="relu"),
+        tensorflow.keras.layers.BatchNormalization(),
+        Dense(512 // 2, activation="relu"),
+        tensorflow.keras.layers.BatchNormalization(),
+        Dense(512 // 4, activation="relu"),
+        tensorflow.keras.layers.BatchNormalization(),
+        Dense(512 // 8, activation="relu"),
+        tensorflow.keras.layers.BatchNormalization(),
+        Dense(32, activation="relu"),
+        tensorflow.keras.layers.BatchNormalization(),
+        Dense(1, activation="linear"),
+    ]
+)
+
+model.compile(
+    loss="mean_squared_error", optimizer="adam", metrics=["mean_squared_error"]
+)
+model.fit(x, y, epochs=100)
+
+x_test = pd.read_csv("test-A/in.tsv", sep="\t", names=in_columns)
+df_train = pd.read_csv("train/in.tsv", names=in_columns, sep="\t")
+
+x_test = pd.concat([x_test, df_train])
+
+x_test = x_test.drop(["nazwa_stacji", "typ_zbioru"], axis=1)
+x_test = pd.get_dummies(x_test, columns=["id_stacji", "rok", "miesiąc"])
+x_test = x_test.iloc[:-8760]
+
+pred = model.predict(x_test)
+out = pd.DataFrame(pred)
+out.to_csv("test-A/out.tsv", sep="\t", header=False, index=False)