Results of the live coding (#2)

.dvc/.gitignore

@@ -0,0 +1,3 @@
+/config.local
+/tmp
+/cache

dvc.lock

@@ -6,54 +6,54 @@ stages:
     deps:
     - path: src/pipeline/preprocess
       hash: md5
-      md5: 8260624b4b5a55764d29de5f1693b825.dir
-      size: 1572
+      md5: c0103315f729a77ca07420c77735a9de.dir
+      size: 1066
       nfiles: 2
     outs:
     - path: data/preprocess
       hash: md5
-      md5: 0d3aad6e7d0f13fa5ee381d7304a94ef.dir
-      size: 99640
+      md5: e3e7f02c0d35afc5cccf9786ffdbc061.dir
+      size: 134027
       nfiles: 3
   train:
     cmd: python -m src.pipeline.train.training --preprocess-folder "data/preprocess/"
       --output-folder "data/train/"
     deps:
     - path: data/preprocess
       hash: md5
-      md5: 0d3aad6e7d0f13fa5ee381d7304a94ef.dir
-      size: 99640
+      md5: e3e7f02c0d35afc5cccf9786ffdbc061.dir
+      size: 134027
       nfiles: 3
     - path: src/pipeline/train
       hash: md5
-      md5: 77e525a708041d3b75310f17c6506b86.dir
-      size: 1469
+      md5: 97cad4658c6c59e05ab81b676d04abca.dir
+      size: 1053
       nfiles: 2
     params:
       params.py:
         SEED: 42
     outs:
     - path: data/train
       hash: md5
-      md5: 93fc32f25b693493ec68313a5492d32b.dir
-      size: 3396162
+      md5: 5f94e76055cef2085b7f2f8e2d1f8678.dir
+      size: 3396047
       nfiles: 1
   evaluate:
     cmd: python -m src.pipeline.evaluate.evaluation --preprocess-folder "data/preprocess"
       --train-folder "data/train/"
     deps:
     - path: data/preprocess
       hash: md5
-      md5: 0d3aad6e7d0f13fa5ee381d7304a94ef.dir
-      size: 99640
+      md5: e3e7f02c0d35afc5cccf9786ffdbc061.dir
+      size: 134027
       nfiles: 3
     - path: data/train
       hash: md5
-      md5: 93fc32f25b693493ec68313a5492d32b.dir
-      size: 3396162
+      md5: 5f94e76055cef2085b7f2f8e2d1f8678.dir
+      size: 3396047
       nfiles: 1
     - path: src/pipeline/evaluate
       hash: md5
-      md5: ea95423ef009670bf0d89fd0a9a3ed50.dir
-      size: 1045
+      md5: 3a95133c38b6b72f12c0451eed8baa27.dir
+      size: 1082
       nfiles: 2

src/pipeline/evaluate/evaluation.py

@@ -1,12 +1,36 @@
 """Module for model evaluation."""
 
+import pickle
 from pathlib import Path
+from statistics import mean
 
+import pandas as pd
 import typer
+from sklearn.metrics import mean_absolute_error
 
 
 def main(preprocess_folder: Path = typer.Option(...), train_folder: Path = typer.Option(...)) -> None:
     """Main function."""
+    df_train = pd.read_parquet(preprocess_folder / "df_train.parquet")
+    df_test = pd.read_parquet(preprocess_folder / "df_test.parquet")
+
+    with open(train_folder / "model.pkl", "rb") as f:
+        model = pickle.load(f)
+    target_column = "average_grade"
+    X_train = df_train.drop(columns=[target_column])
+    y_train = df_train[target_column]
+    model.fit(X_train, y_train)
+
+    X_test = df_test.drop(columns=[target_column])
+    y_test = df_test[target_column]
+    predictions = model.predict(X_test)
+
+    baseline_pred = y_train.mean()
+
+    mae = mean_absolute_error(y_test, predictions)
+    mae_baseline = mean_absolute_error(y_test, [baseline_pred] * len(y_test))
+
+    print(f"{mae=}, {mae_baseline=}")
 
 
 if __name__ == "__main__":

src/pipeline/preprocess/preprocessing.py

@@ -2,13 +2,31 @@
 
 from pathlib import Path
 
+import numpy as np
+import pandas as pd
 import typer
+from sklearn.model_selection import train_test_split
 
 
 def main(input_folder: Path = typer.Option(...), output_folder: Path = typer.Option(...)) -> None:
     """Main function."""
     output_folder.mkdir(exist_ok=True, parents=True)
 
+    df = pd.read_csv(input_folder / "student-por.csv")
+
+    categorical_variables = [col for col, dtype in df.items() if not np.issubdtype(dtype, np.number)]
+    df = pd.get_dummies(df, columns=categorical_variables, drop_first=False, dtype=float)
+
+    df["average_grade"] = (df["G1"] + df["G2"] + df["G3"]) / 3.0
+    df = df.drop(["G1", "G2", "G3"], axis="columns")
+
+    df_train, df_test = train_test_split(df, test_size=0.2)
+    df_train, df_val = train_test_split(df_train, test_size=0.2)
+
+    df_train.to_parquet(output_folder / "df_train.parquet")
+    df_val.to_parquet(output_folder / "df_val.parquet")
+    df_test.to_parquet(output_folder / "df_test.parquet")
+
 
 if __name__ == "__main__":
     typer.run(main)

src/pipeline/train/training.py

@@ -1,14 +1,37 @@
 """Module for training the model."""
 
+import pickle
 from pathlib import Path
 
+import pandas as pd
 import typer
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_absolute_error
 
 
 def main(preprocess_folder: Path = typer.Option(...), output_folder: Path = typer.Option(...)) -> None:
     """Main function."""
     output_folder.mkdir(exist_ok=True, parents=True)
 
+    df_train = pd.read_parquet(preprocess_folder / "df_train.parquet")
+    df_val = pd.read_parquet(preprocess_folder / "df_val.parquet")
+
+    model = RandomForestRegressor()
+
+    target_column = "average_grade"
+    X_train = df_train.drop(columns=[target_column])
+    y_train = df_train[target_column]
+    model.fit(X_train, y_train)
+
+    X_val = df_val.drop(columns=[target_column])
+    y_val = df_val[target_column]
+    predictions = model.predict(X_val)
+
+    mae = mean_absolute_error(y_val, predictions)
+    print(mae)
+    with open(output_folder / "model.pkl", "wb") as f:
+        pickle.dump(model, f)
+
 
 if __name__ == "__main__":
     typer.run(main)