Update stuff_model/stuff_apply.py

stuff_model/stuff_apply.py

@@ -1,57 +1,57 @@
-import polars as pl
-import joblib
-
-model = joblib.load('stuff_model/stuff_model.joblib')
-# Read the values from the text file
-with open('stuff_model/target_stats.txt', 'r') as file:
-    lines = file.readlines()
-    target_mean = float(lines[0].strip())
-    target_std = float(lines[1].strip())
-    
-# Define the features to be used for training
-features = ['start_speed',
-            'spin_rate',
-            'extension',
-            'ivb',
-            'hb',
-            'x0',
-            'z0',
-            'speed_diff',
-            'ivb_diff',
-            'hb_diff']
-
-
-def stuff_apply(df:pl.DataFrame) -> pl.DataFrame:
-    # Filter the dataframe to include only the rows for the year 2024 and drop rows with null values in the specified features and target column
-    # df_test = df.drop_nulls(subset=features)
-    df_test = df.clone()
-
-    # Predict the target values for the 2024 data using the trained model
-    df_test = df_test.with_columns(
-        pl.Series(name="target", values=model.predict(df_test[features].to_numpy()))
-    )
-    # Standardize the target column to create a z-score
-    df_test = df_test.with_columns(
-        ((pl.col('target') - target_mean) / target_std).alias('target_zscore')
-    )
-
-    # Convert the z-score to tj_stuff_plus
-    df_test = df_test.with_columns(
-        (100 - (pl.col('target_zscore') * 10)).alias('tj_stuff_plus')
-    )
-
-    df_pitch_types = pl.read_csv('stuff_model/tj_stuff_plus_pitch.csv')
-
-    # Join the pitch type statistics with the main DataFrame based on pitch_type
-    df_pitch_all = df_test.join(df_pitch_types, left_on='pitch_type', right_on='pitch_type')
-
-    # Normalize pitch_grade values to a range between -0.5 and 0.5 based on the percentiles
-    df_pitch_all = df_pitch_all.with_columns(
-        ((pl.col('tj_stuff_plus') - pl.col('mean')) / pl.col('std')).alias('pitch_grade')
-    )
-
-    # Scale the pitch_grade values to a range between 20 and 80
-    df_pitch_all = df_pitch_all.with_columns(
-        (pl.col('pitch_grade') * 10 + 50).clip(20, 80)
-    )
+import polars as pl
+import joblib
+
+model = joblib.load('stuff_model/stuff_model.joblib')
+# Read the values from the text file
+with open('stuff_model/target_stats.txt', 'r') as file:
+    lines = file.readlines()
+    target_mean = float(lines[0].strip())
+    target_std = float(lines[1].strip())
+    
+# Define the features to be used for training
+features = ['start_speed',
+            'spin_rate',
+            'extension',
+            'ivb',
+            'hb',
+            'x0',
+            'z0',
+            'speed_diff',
+            'ivb_diff',
+            'hb_diff']
+
+
+def stuff_apply(df:pl.DataFrame) -> pl.DataFrame:
+    # Filter the dataframe to include only the rows for the year 2024 and drop rows with null values in the specified features and target column
+    # df_test = df.drop_nulls(subset=features)
+    df_test = df.clone()
+
+    # Predict the target values for the 2024 data using the trained model
+    df_test = df_test.with_columns(
+        pl.Series(name="target", values=model.predict(df_test[features].to_numpy()))
+    )
+    # Standardize the target column to create a z-score
+    df_test = df_test.with_columns(
+        ((pl.col('target') - target_mean) / target_std).alias('target_zscore')
+    )
+
+    # Convert the z-score to tj_stuff_plus
+    df_test = df_test.with_columns(
+        (100 - (pl.col('target_zscore') * 10)).alias('tj_stuff_plus')
+    )
+
+    df_pitch_types = pl.read_csv('stuff_model/tj_stuff_plus_pitch.csv')
+
+    # Join the pitch type statistics with the main DataFrame based on pitch_type
+    df_pitch_all = df_test.join(df_pitch_types, left_on='pitch_type', right_on='pitch_type')
+
+    # Normalize pitch_grade values to a range between -0.5 and 0.5 based on the percentiles
+    df_pitch_all = df_pitch_all.with_columns(
+        ((pl.col('tj_stuff_plus') - pl.col('mean')) / (pl.col('std')/2)).alias('pitch_grade')
+    )
+
+    # Scale the pitch_grade values to a range between 20 and 80
+    df_pitch_all = df_pitch_all.with_columns(
+        (pl.col('pitch_grade') * 10 + 50).clip(20, 80)
+    )
     return df_pitch_all