fix

model.py

@@ -1,3 +1,44 @@
+# import sys
+# import pandas as pd
+# from sklearn.model_selection import train_test_split
+# from sklearn.preprocessing import StandardScaler, OneHotEncoder
+# from sklearn.compose import ColumnTransformer
+# from sklearn.pipeline import Pipeline
+# from tensorflow.keras.models import Sequential
+# from tensorflow.keras.layers import Dense
+# import tensorflow as tf
+#
+# data = pd.read_csv('./data/train.csv')
+#
+# data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
+#
+# features = data[['Sex', 'Age', 'BodyweightKg']]
+# target = data['TotalKg']
+#
+# X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
+#
+# preprocessor = ColumnTransformer(
+#     transformers=[
+#         ('num', StandardScaler(), ['Age', 'BodyweightKg']),
+#         ('cat', OneHotEncoder(), ['Sex'])
+#     ]
+# )
+#
+# pipeline = Pipeline(steps=[
+#     ('preprocessor', preprocessor),
+#     ('model', Sequential([
+#         Dense(64, activation='relu', input_dim=4),
+#         Dense(64, activation='relu'),
+#         Dense(1)
+#     ]))
+# ])
+#
+# pipeline['model'].compile(optimizer='adam', loss='mse', metrics=['mae'])
+#
+# pipeline.fit(X_train, y_train, model__epochs=int(sys.argv[1]), model__validation_split=0.1)
+#
+# pipeline['model'].save('powerlifting_model.h5')
+
 import sys
 import pandas as pd
 from sklearn.model_selection import train_test_split
@@ -10,17 +51,20 @@ import tensorflow as tf
 
 data = pd.read_csv('./data/train.csv')
 
-data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
+features_idx = [1, 4, 7]  # Sex, Age, BodyweightKg
+target_idx = 25  # TotalKg
 
-features = data[['Sex', 'Age', 'BodyweightKg']]
+data = data.iloc[:, [1, 4, 7, 25]].dropna()
-target = data['TotalKg']
+
+features = data.iloc[:, features_idx]
+target = data.iloc[:, target_idx]
 
 X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
 
 preprocessor = ColumnTransformer(
     transformers=[
-        ('num', StandardScaler(), ['Age', 'BodyweightKg']),
+        ('num', StandardScaler(), [1, 2]),  # Age, BodyweightKg
-        ('cat', OneHotEncoder(), ['Sex'])
+        ('cat', OneHotEncoder(), [0])  # Sex
     ]
 )
 
@@ -38,3 +82,4 @@ pipeline['model'].compile(optimizer='adam', loss='mse', metrics=['mae'])
 pipeline.fit(X_train, y_train, model__epochs=int(sys.argv[1]), model__validation_split=0.1)
 
 pipeline['model'].save('powerlifting_model.h5')
+