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: import pandas as pd import numpy as np from scipy import stats def compute_rolling_variance( df, horse_col='horse_id', date_col='race_date', result_col='result', ddof=0, max_past_races=10 ): # エラーチェック for col in [horse_col, date_col, result_col]: if col not in df.columns: raise ValueError(f"列 '{col}' がデータフレームに存在しません") if not pd.api.types.is_numeric_dtype(df[result_col]): raise ValueError(f"列 '{result_col}' は数値型である必要があります") if not pd.api.types.is_datetime64_any_dtype(df[date_col]): raise ValueError(f"列 '{date_col}' は datetime 型である必要があります") # 時系列でソート（馬ごとに） df_sorted = df.sort_values(by=[horse_col, date_col]).copy() # 結果格納用 past_variances = [] # 馬ごとに処理 for _, group in df_sorted.groupby(horse_col): past_results = [] for value in group[result_col]: if past_results: var = np.var(past_results[-max_past_races:], ddof=ddof) else: var = np.nan past_variances.append(var) past_results.append(value) # 現在の値を追加（次回以降用） df_sorted['past_variance'] = past_variances return df_sorted def is_improving_trend( df, horse_col='horse_id', date_col='日付', result_col='result', lookback=4 ): half = lookback // 2 for col in [horse_col, date_col, result_col]: if col not in df.columns: raise ValueError(f"列 '{col}' がデータフレームに存在しません") if not pd.api.types.is_numeric_dtype(df[result_col]): raise ValueError(f"列 '{result_col}' は数値型である必要があります") if not pd.api.types.is_datetime64_any_dtype(df[date_col]): raise ValueError(f"列 '{date_col}' は datetime 型である必要があります") df_sorted = df.sort_values(by=[horse_col, date_col]).copy() trend_flags = [] slopes = [] levels = [] stds = [] modes = [] for _, group in df_sorted.groupby(horse_col): past_results = [] for value in group[result_col]: if len(past_results) >= lookback: past = past_results[-lookback:] first_half = [v for v in past[:half] if pd.notna(v)] second_half = [v for v in past[half:] if pd.notna(v)] # 上がり調子判定 if len(first_half) >= 1 and len(second_half) >= 1: avg_before = np.mean(first_half) avg_after = np.mean(second_half) flag = avg_after > avg_before else: flag = np.nan # 傾き・平均 y = np.array([v for v in past if pd.notna(v)]) if len(y) >= 2: x = np.arange(len(y)) slope = np.polyfit(x, y, 1)[0] level = np.mean(y) std = np.std(y, ddof=1) # 修正前（NG） # mode = stats.mode(y, nan_policy='omit', keepdims=False).mode # 修正後（OK） mode_result = stats.mode(y, nan_policy='omit') mode = mode_result.mode[0] if len(mode_result.mode) > 0 else np.nan else: slope = np.nan level = np.nan std = np.nan mode = np.nan else: flag = np.nan slope = np.nan level = np.nan std = np.nan mode = np.nan trend_flags.append(flag) slopes.append(slope) levels.append(level) stds.append(std) modes.append(mode) past_results.append(value) df_sorted["is_improving"] = trend_flags df_sorted["trend_slope"] = slopes df_sorted["trend_level"] = levels df_sorted["trend_std"] = stds df_sorted["trend_mode"] = modes df_sorted["race_count"] = df_sorted.groupby(horse_col).cumcount() + 1 return df_sorted def compute_features(group): group = group.sort_values('日付') features_list = [] # 最初の行（最新のレース結果）から順に特徴量を計算 for idx, row in group.iterrows(): temp_group = group.loc[group['日付'] <= row['日付']] # 最新のタイムポイントまでのデータ temp_group = temp_group.sort_values('日付') # 特徴量計算 temp_group['前走間隔'] = temp_group['日付'].diff().dt.days temp_group['前走と前々走の間隔'] = temp_group['前走間隔'].diff() temp_group['平均間隔_3走'] = temp_group['前走間隔'].rolling(3).mean() temp_group['同一競馬場'] = temp_group['開催場所'] == temp_group['開催場所'].shift(1) temp_group['連続競馬場'] = temp_group['開催場所'] == temp_group['開催場所'].shift(2) temp_group['競馬場変化回数'] = temp_group['開催場所'].ne(temp_group['開催場所'].shift()).cumsum() good_weight = temp_group.loc[temp_group['着順'] <= 3, '馬体重'].mean() bad_weight = temp_group.loc[temp_group['着順'] > 10, '馬体重'].mean() good_dist = temp_group.loc[temp_group['着順'] <= 3].groupby('距離').size().idxmax() if not temp_group.loc[temp_group['着順'] <= 3].empty else np.nan bad_dist = temp_group.loc[temp_group['着順'] > 10].groupby('距離').size().idxmax() if not temp_group.loc[temp_group['着順'] > 10].empty else np.nan good_dist_perf = temp_group.loc[temp_group['距離'] == good_dist, '着順'].mean() if not pd.isna(good_dist) else np.nan bad_dist_perf = temp_group.loc[temp_group['距離'] == bad_dist, '着順'].mean() if not pd.isna(bad_dist) else np.nan dist_perf = temp_group.groupby('距離')['着順'].mean() 得意距離 = dist_perf.idxmin() if not dist_perf.empty else np.nan good_jockey = temp_group.loc[temp_group['着順'] <= 3].groupby('騎手').size().idxmax() if not temp_group.loc[temp_group['着順'] <= 3].empty else np.nan bad_jockey = temp_group.loc[temp_group['着順'] > 10].groupby('騎手').size().idxmax() if not temp_group.loc[temp_group['着順'] > 10].empty else np.nan good_jockey_perf = temp_group.loc[temp_group['騎手'] == good_jockey, '着順'].mean() if not pd.isna(good_jockey) else np.nan bad_jockey_perf = temp_group.loc[temp_group['騎手'] == bad_jockey, '着順'].mean() if not pd.isna(bad_jockey) else np.nan good_season = temp_group.loc[temp_group['着順'] <= 3].groupby('季節').size().idxmax() if not temp_group.loc[temp_group['着順'] <= 3].empty else np.nan bad_season = temp_group.loc[temp_group['着順'] > 10].groupby('季節').size().idxmax() if not temp_group.loc[temp_group['着順'] > 10].empty else np.nan good_season_perf = temp_group.loc[temp_group['季節'] == good_season, '着順'].mean() if not pd.isna(good_season) else np.nan bad_season_perf = temp_group.loc[temp_group['季節'] == bad_season, '着順'].mean() if not pd.isna(bad_season) else np.nan # 計算された特徴量をリストに追加 features_list.append({ 'horse_id': row['horse_id'], # 馬ID '日付': row['日付'], # レース日 '出走回数': len(temp_group), '成績上位時馬体重': good_weight, '成績下位時馬体重': bad_weight, '成績の良い距離': good_dist, '成績の悪い距離': bad_dist, '成績の良い距離の成績': good_dist_perf, '成績の悪い距離の成績': bad_dist_perf, '得意そうな距離': 得意距離, '相性の良い騎手': good_jockey, '相性の良い騎手の成績': good_jockey_perf, '相性の悪い騎手': bad_jockey, '相性の悪い騎手の成績': bad_jockey_perf, '得意な季節': good_season, '得意な季節の成績': good_season_perf, '不得意な季節': bad_season, '不得意な季節の成績': bad_season_perf, }) return pd.DataFrame(features_list) def compute_features_vectorized(group): group = group.sort_values(['horse_id', '日付']).copy() # 前走間隔と間隔の差 group['前走間隔'] = group.groupby('horse_id')['日付'].diff().dt.days group['前走と前々走の間隔'] = group.groupby('horse_id')['前走間隔'].diff() # 平均間隔（直近3走） group['平均間隔_3走'] = group.groupby('horse_id')['前走間隔'].rolling(3, min_periods=1).mean().reset_index(level=0, drop=True) # 同一競馬場、連続競馬場 group['同一競馬場'] = group.groupby('horse_id')['開催場所'].transform(lambda x: x == x.shift(1)) group['連続競馬場'] = group.groupby('horse_id')['開催場所'].transform(lambda x: x == x.shift(2)) group['競馬場変化回数'] = group.groupby('horse_id')['開催場所'].apply(lambda x: x.ne(x.shift()).cumsum()) # 上位成績・下位成績のフラグ group['上位'] = group['着順'] <= 3 group['下位'] = group['着順'] > 10 # 馬体重平均 group['成績上位時馬体重'] = group.groupby('horse_id')['馬体重'].transform(lambda x: x[group.loc[x.index, '上位']].expanding().mean()).reset_index(level=0, drop=True) group['成績下位時馬体重'] = group.groupby('horse_id')['馬体重'].transform(lambda x: x[group.loc[x.index, '下位']].expanding().mean()).reset_index(level=0, drop=True) # 得意・不得意距離、成績 def get_best_worst_perf(df, flag_col, group_col): def inner(sub): sub = sub.loc[sub[flag_col]] if sub.empty: return pd.Series([np.nan, np.nan]) counts = sub.groupby(group_col).size() best = counts.idxmax() perf = df[df[group_col] == best]['着順'].expanding().mean().iloc[-1] return pd.Series([best, perf]) return df.groupby('horse_id').apply(inner).rename(columns={0: f'{flag_col}_{group_col}', 1: f'{flag_col}_{group_col}_perf'}) # 得意距離（平均着順が最も良い距離） def get_preferred_distance(group): res = [] for _, g in group.groupby('horse_id'): d = g.groupby('距離')['着順'].mean() 得意距離 = d.idxmin() if not d.empty else np.nan res.extend([得意距離]*len(g)) return res group['得意そうな距離'] = get_preferred_distance(group) # 騎手・季節：同様に処理（略） # 出走回数 group['出走回数'] = group.groupby('horse_id').cumcount() + 1 return group[[ 'horse_id', '日付', '出走回数', '前走間隔', '前走と前々走の間隔', '平均間隔_3走', '同一競馬場', '連続競馬場', '競馬場変化回数', '成績上位時馬体重', '成績下位時馬体重', '得意そうな距離' # 必要に応じて、騎手・季節などの項目を追加 ]]（2025-05-07 07:47）
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: def today_rolling_df(base,target_df,target_columns,rolling_column_list_list=[]): #↓reindexは既存のカラムへの影響が強いため検討が必要 df = base.reinex(target_df) # 各ターゲットカラムのIDリストを取得 target_id_lists = [df[col] for col in target_columns] # クエリを動的に作成 query = ' | '.join([f'{target_columns[i]} in @target_id_lists[{i}]' for i in range(len(target_columns))]) # クエリを実行 target_df = target_df.query(query) concat_df = pd.concat([df,target_df],axis=1) rolling_df = rolling_master(concat_df,rolling_column_list_list) return rolling_df def today_making_master(base,target_base,pre_group_list_list=[]): """ 処理の流れ ①target_dfを今回出走する馬の情報に限定する。 ②前走出走日を取得する。 ③以降同じ流れ """ hores_id_list = base["horse_id"] temp = target_base.query("@horse_id in horse_id_list") merge_df = pd.concat([base,temp],axis=1) merge_df = getting_pre_date(merge_df) target_df = making_pre_df(target_base) merge_df = combine_noyatu(merge_df,target_df,on=["horse_id","日付"],how="left") return merge_df（2025-04-24 12:52）
: def today_rolling_df(base,target_df,target_columns,rolling_column_list_list=[]): #↓reindexは既存のカラムへの影響が強いため検討が必要 df = base.reinex(target_df) # 各ターゲットカラムのIDリストを取得 target_id_lists = [df[col] for col in target_columns] # クエリを動的に作成 query = ' | '.join([f'target_id_{i+1} in @target_id_lists[{i}]' for i in range(len(target_columns))]) # クエリを実行 target_df = target_df.query(query) concat_df = pd.concat([df,target_df],axis=1) rolling_df = rolling_master(concat_df,rolling_column_list_list) return rolling_df def today_making_master(base,target_base,pre_group_list_list=[]): """ 処理の流れ ①target_dfを今回出走する馬の情報に限定する。 ②前走出走日を取得する。 ③以降同じ流れ """ hores_id_list = base["horse_id"] temp = target_base.query("@horse_id in horse_id_list") merge_df = pd.concat([base,temp],axis=1) merge_df = getting_pre_date(merge_df) target_df = making_pre_df(target_base) merge_df = combine_noyatu(merge_df,target_df,on=["horse_id","日付"],how="left") return merge_df（2025-04-24 12:41）
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管理者: 気軽に質問とかしてくれたらうれしいです！（2025-02-13 22:45）