Update stuff_model/feature_engineering.py

stuff_model/feature_engineering.py

@@ -1,118 +1,131 @@
-import polars as pl
-import numpy as np
-
-def feature_engineering(df: pl.DataFrame) -> pl.DataFrame:
-    # Extract the year from the game_date column
-    df = df.with_columns(
-        pl.col('game_date').str.slice(0, 4).alias('year')
-    )
-
-    df = df.with_columns([
-        
-        (-(pl.col('vy0')**2 - (2 * pl.col('ay') * (pl.col('y0') - 17/12)))**0.5).alias('vy_f'),
-        ])
-
-    df = df.with_columns([
-        ((pl.col('vy_f') - pl.col('vy0')) / pl.col('ay')).alias('t'),
-        ])
-
-    df = df.with_columns([
-        (pl.col('vz0') + (pl.col('az') * pl.col('t'))).alias('vz_f'),
-        (pl.col('vx0') + (pl.col('ax') * pl.col('t'))).alias('vx_f')
-        ])
-
-    df = df.with_columns([
-            (-np.arctan(pl.col('vz_f') / pl.col('vy_f')) * (180 / np.pi)).alias('vaa'),
-            (-np.arctan(pl.col('vx_f') / pl.col('vy_f')) * (180 / np.pi)).alias('haa')
-        ])
-
-    # Mirror horizontal break for left-handed pitchers
-    df = df.with_columns(
-        pl.when(pl.col('pitcher_hand') == 'L')
-        .then(-pl.col('ax'))
-        .otherwise(pl.col('ax'))
-        .alias('ax')
-    )
-
-    # Mirror horizontal break for left-handed pitchers
-    df = df.with_columns(
-        pl.when(pl.col('pitcher_hand') == 'L')
-        .then(-pl.col('hb'))
-        .otherwise(pl.col('hb'))
-        .alias('hb')
-    )
-
-    # Mirror horizontal release point for left-handed pitchers
-    df = df.with_columns(
-        pl.when(pl.col('pitcher_hand') == 'L')
-        .then(pl.col('x0'))
-        .otherwise(-pl.col('x0'))
-        .alias('x0')
-    )
-
-    # Define the pitch types to be considered
-    pitch_types = ['SI', 'FF', 'FC']
-
-    # Filter the DataFrame to include only the specified pitch types
-    df_filtered = df.filter(pl.col('pitch_type').is_in(pitch_types))
-
-    # Group by pitcher_id and year, then aggregate to calculate average speed and usage percentage
-    df_agg = df_filtered.group_by(['pitcher_id', 'year', 'pitch_type']).agg([
-        pl.col('start_speed').mean().alias('avg_fastball_speed'),
-        pl.col('az').mean().alias('avg_fastball_az'),
-        pl.col('ax').mean().alias('avg_fastball_ax'),
-        pl.len().alias('count')
-    ])
-
-    # Sort the aggregated data by count and average fastball speed
-    df_agg = df_agg.sort(['count', 'avg_fastball_speed'], descending=[True, True])
-    df_agg = df_agg.unique(subset=['pitcher_id', 'year'], keep='first')
-
-    # Join the aggregated data with the main DataFrame
-    df = df.join(df_agg, on=['pitcher_id', 'year'])
-
-    # If no fastball, use the fastest pitch for avg_fastball_speed
-    df = df.with_columns(
-        pl.when(pl.col('avg_fastball_speed').is_null())
-        .then(pl.col('start_speed').max().over('pitcher_id'))
-        .otherwise(pl.col('avg_fastball_speed'))
-        .alias('avg_fastball_speed')
-    )
-
-    # If no fastball, use the fastest pitch for avg_fastball_az
-    df = df.with_columns(
-        pl.when(pl.col('avg_fastball_az').is_null())
-        .then(pl.col('az').max().over('pitcher_id'))
-        .otherwise(pl.col('avg_fastball_az'))
-        .alias('avg_fastball_az')
-    )
-
-    # If no fastball, use the fastest pitch for avg_fastball_ax
-    df = df.with_columns(
-        pl.when(pl.col('avg_fastball_ax').is_null())
-        .then(pl.col('ax').max().over('ax'))
-        .otherwise(pl.col('avg_fastball_ax'))
-        .alias('avg_fastball_ax')
-    )
-
-    # Calculate pitch differentials
-    df = df.with_columns(
-        (pl.col('start_speed') - pl.col('avg_fastball_speed')).alias('speed_diff'),
-        (pl.col('az') - pl.col('avg_fastball_az')).alias('az_diff'),
-        (pl.col('ax') - pl.col('avg_fastball_ax')).abs().alias('ax_diff')
-    )
-
-    # Cast the year column to integer type
-    df = df.with_columns(
-        pl.col('year').cast(pl.Int64)
-    )
-
-
-    
-    df = df.with_columns([
-        pl.lit('All').alias('all')
-    ])
-
-
-    
+import polars as pl
+import numpy as np
+
+def feature_engineering(df: pl.DataFrame) -> pl.DataFrame:
+    # Extract the year from the game_date column
+    df = df.with_columns(
+        pl.col('game_date').str.slice(0, 4).alias('year')
+    )
+
+    df = df.with_columns([
+        
+        (-(pl.col('vy0')**2 - (2 * pl.col('ay') * (pl.col('y0') - 17/12)))**0.5).alias('vy_f'),
+        ])
+
+    df = df.with_columns([
+        ((pl.col('vy_f') - pl.col('vy0')) / pl.col('ay')).alias('t'),
+        ])
+
+    df = df.with_columns([
+        (pl.col('vz0') + (pl.col('az') * pl.col('t'))).alias('vz_f'),
+        (pl.col('vx0') + (pl.col('ax') * pl.col('t'))).alias('vx_f')
+        ])
+
+    df = df.with_columns([
+            (-np.arctan(pl.col('vz_f') / pl.col('vy_f')) * (180 / np.pi)).alias('vaa'),
+            (-np.arctan(pl.col('vx_f') / pl.col('vy_f')) * (180 / np.pi)).alias('haa')
+        ])
+
+    # Mirror horizontal break for left-handed pitchers
+    df = df.with_columns(
+        pl.when(pl.col('pitcher_hand') == 'L')
+        .then(-pl.col('ax'))
+        .otherwise(pl.col('ax'))
+        .alias('ax')
+    )
+
+    # Mirror horizontal break for left-handed pitchers
+    df = df.with_columns(
+        pl.when(pl.col('pitcher_hand') == 'L')
+        .then(-pl.col('hb'))
+        .otherwise(pl.col('hb'))
+        .alias('hb')
+    )
+
+    # Mirror horizontal release point for left-handed pitchers
+    df = df.with_columns(
+        pl.when(pl.col('pitcher_hand') == 'L')
+        .then(pl.col('x0'))
+        .otherwise(-pl.col('x0'))
+        .alias('x0')
+    )
+
+    # Define the pitch types to be considered
+    pitch_types = ['SI', 'FF', 'FC']
+
+    # Filter the DataFrame to include only the specified pitch types
+    df_filtered = df.filter(pl.col('pitch_type').is_in(pitch_types))
+
+    # Group by pitcher_id and year, then aggregate to calculate average speed and usage percentage
+    df_agg = df_filtered.group_by(['pitcher_id', 'year', 'pitch_type']).agg([
+        pl.col('start_speed').mean().alias('avg_fastball_speed'),
+        pl.col('az').mean().alias('avg_fastball_az'),
+        pl.col('ax').mean().alias('avg_fastball_ax'),
+        pl.len().alias('count')
+    ])
+
+    # Sort the aggregated data by count and average fastball speed
+    df_agg = df_agg.sort(['count', 'avg_fastball_speed'], descending=[True, True])
+    df_agg = df_agg.unique(subset=['pitcher_id', 'year'], keep='first')
+
+    # Join the aggregated data with the main DataFrame
+    df = df.join(df_agg, on=['pitcher_id', 'year'])
+
+    # If no fastball, use the fastest pitch for avg_fastball_speed
+    df = df.with_columns(
+        pl.when(pl.col('avg_fastball_speed').is_null())
+        .then(pl.col('start_speed').max().over('pitcher_id'))
+        .otherwise(pl.col('avg_fastball_speed'))
+        .alias('avg_fastball_speed')
+    )
+
+    # If no fastball, use the fastest pitch for avg_fastball_az
+    df = df.with_columns(
+        pl.when(pl.col('avg_fastball_az').is_null())
+        .then(pl.col('az').max().over('pitcher_id'))
+        .otherwise(pl.col('avg_fastball_az'))
+        .alias('avg_fastball_az')
+    )
+
+    # If no fastball, use the fastest pitch for avg_fastball_ax
+    df = df.with_columns(
+        pl.when(pl.col('avg_fastball_ax').is_null())
+        .then(pl.col('ax').max().over('ax'))
+        .otherwise(pl.col('avg_fastball_ax'))
+        .alias('avg_fastball_ax')
+    )
+
+    # Calculate pitch differentials
+    df = df.with_columns(
+        (pl.col('start_speed') - pl.col('avg_fastball_speed')).alias('speed_diff'),
+        (pl.col('az') - pl.col('avg_fastball_az')).alias('az_diff'),
+        (pl.col('ax') - pl.col('avg_fastball_ax')).abs().alias('ax_diff')
+    )
+
+    # Cast the year column to integer type
+    df = df.with_columns(
+        pl.col('year').cast(pl.Int64)
+    )
+
+
+    
+    df = df.with_columns([
+        pl.lit('All').alias('all')
+    ])
+
+    # Calculate mound_to_release as 60.5 - extension
+    df = df.with_columns([
+        (60.5 - df["extension"]).alias("release_pos_y")
+    ])
+    
+    # Calculate delta time (Δt)
+    delta_t = (df["release_pos_y"] - df["y0"]) / df["vy0"]
+
+    # Corrected back-calculation of release_pos_x and release_pos_z
+    df = df.with_columns([
+        (df["x0"] + df["vx0"] * delta_t + 0.5 * df["ax"] * delta_t ** 2).alias("release_pos_x"),
+        (df["z0"] + df["vz0"] * delta_t + 0.5 * df["az"] * delta_t ** 2).alias("release_pos_z")
+    ])
+
+    
     return df