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@@ -267,4 +267,236 @@ def predict_data_simple(df_input, city_pair, output_dir, predict_dir=".", pred_t
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m = all_prices['source'] == 'rise'
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df_rise_nodes.loc[all_prices.loc[m, 'row_id'], 'relative_position'] = all_prices.loc[m, 'relative_position'].values
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- pass
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+ pass
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+ # =====================================================================
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+
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+ df_min_hours['simple_will_price_drop'] = 0
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+ df_min_hours['simple_drop_in_hours'] = 0
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+ df_min_hours['simple_drop_in_hours_prob'] = 0.0
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+ df_min_hours['simple_drop_in_hours_dist'] = '' # 空串 表示未知
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+ df_min_hours['flag_dist'] = ''
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+ df_min_hours['drop_price_change_upper'] = 0.0
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+ df_min_hours['drop_price_change_lower'] = 0.0
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+ df_min_hours['drop_price_sample_size'] = 0
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+ df_min_hours['rise_price_change_upper'] = 0.0
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+ df_min_hours['rise_price_change_lower'] = 0.0
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+ df_min_hours['rise_price_sample_size'] = 0
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+
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+ # 这个阈值取多少?
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+ # pct_threshold = 0.01
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+ # pct_threshold_1 = 0.01
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+
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+ for idx, row in df_min_hours.iterrows():
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+ city_pair = row['citypair']
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+ flight_numbers = row['flight_numbers']
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+ baggage_weight = row['baggage_weight']
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+ days_to_departure = row['days_to_departure']
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+ hours_until_departure = row['hours_until_departure']
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+ price_change_percent = row['price_change_percent']
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+ price_change_amount = row['price_change_amount']
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+ price_duration_hours = row['price_duration_hours']
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+ price_amount = row['price_total']
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+
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+ length_drop = 0
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+ length_rise = 0
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+
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+ # 针对历史上发生的 >降价
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+ if not df_drop_nodes.empty:
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+ # 对准航线 航班号 行李配额
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+ df_drop_nodes_part = df_drop_nodes[
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+ (df_drop_nodes['citypair'] == city_pair) &
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+ (df_drop_nodes['flight_numbers'] == flight_numbers) &
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+ (df_drop_nodes['baggage_weight'] == baggage_weight)
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+ ]
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+ # 降价前 增量阈值、当前阈值 的匹配
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+ if not df_drop_nodes_part.empty and pd.notna(price_change_amount):
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+
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+ pca_base = float(price_change_amount)
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+ pca_vals = pd.to_numeric(df_drop_nodes_part['high_price_change_amount'], errors='coerce')
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+ df_drop_gap = df_drop_nodes_part.loc[
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+ pca_vals.notna(),
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+ ['drop_days_to_departure', 'drop_hours_until_departure', 'drop_price_change_percent', 'drop_price_change_amount',
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+ 'high_price_duration_hours', 'high_price_change_percent', 'high_price_change_amount', 'high_price_amount', 'relative_position'
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+ ]
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+ ].copy()
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+ df_drop_gap['pca_gap'] = (pca_vals.loc[pca_vals.notna()] - pca_base)
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+ df_drop_gap['pca_abs_gap'] = df_drop_gap['pca_gap'].abs()
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+
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+ price_base = pd.to_numeric(price_amount, errors='coerce')
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+ high_price_vals = pd.to_numeric(df_drop_gap['high_price_amount'], errors='coerce')
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+ df_drop_gap['price_gap'] = high_price_vals - price_base
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+ df_drop_gap['price_abs_gap'] = df_drop_gap['price_gap'].abs()
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+
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+ df_drop_gap = df_drop_gap.sort_values(['price_abs_gap', 'pca_abs_gap'], ascending=[True, True])
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+ df_match = df_drop_gap[(df_drop_gap['price_abs_gap'] <= 5.0) & (df_drop_gap['pca_abs_gap'] <= 10.0)].copy()
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+
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+ # 历史上出现的极近似的增长(下降)幅度后的降价场景
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+ if not df_match.empty:
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+ dur_base = pd.to_numeric(price_duration_hours, errors='coerce')
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+ # hud_base = pd.to_numeric(hours_until_departure, errors='coerce')
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+ dtd_base = pd.to_numeric(days_to_departure, errors='coerce')
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+
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+ if pd.notna(dur_base) and pd.notna(dtd_base):
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+ df_match_chk = df_match.copy()
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+
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+ drop_dtd_vals = pd.to_numeric(df_match_chk['drop_days_to_departure'], errors='coerce')
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+ df_match_chk = df_match_chk.loc[drop_dtd_vals.notna()].copy()
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+ df_match_chk = df_match_chk.loc[(drop_dtd_vals.loc[drop_dtd_vals.notna()] - float(dtd_base)).abs() <= 3].copy()
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+
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+ # 距离起飞天数也对的上
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+ if not df_match_chk.empty:
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+ length_drop = df_match_chk.shape[0]
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+ df_min_hours.loc[idx, 'drop_price_sample_size'] = length_drop
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+
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+ drop_price_change_upper = df_match_chk['drop_price_change_amount'].max() # 降价上限
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+ drop_price_change_lower = df_match_chk['drop_price_change_amount'].min() # 降价下限
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+ df_min_hours.loc[idx, 'drop_price_change_upper'] = round(drop_price_change_upper, 2)
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+ df_min_hours.loc[idx, 'drop_price_change_lower'] = round(drop_price_change_lower, 2)
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+
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+ remaining_hours = (
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+ pd.to_numeric(df_match_chk['high_price_duration_hours'], errors='coerce') - float(dur_base)
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+ ).clip(lower=0)
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+ remaining_hours = remaining_hours.round().astype(int)
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+
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+ counts = remaining_hours.value_counts().sort_index()
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+ probs = (counts / counts.sum()).round(4)
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+
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+ top_hours = int(probs.idxmax())
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+ top_prob = float(probs.max())
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+
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+ dist_items = list(zip(probs.index.tolist(), probs.tolist()))
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+ dist_items = dist_items[:10]
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+ dist_str = ' '.join([f"{int(h)}h->{float(p)}" for h, p in dist_items])
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+
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+ df_min_hours.loc[idx, 'simple_will_price_drop'] = 1
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+ df_min_hours.loc[idx, 'simple_drop_in_hours'] = top_hours
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+ df_min_hours.loc[idx, 'simple_drop_in_hours_prob'] = 1
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+ df_min_hours.loc[idx, 'simple_drop_in_hours_dist'] = dist_str
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+ df_min_hours.loc[idx, 'flag_dist'] = 'd0'
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+ pass
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+ pass
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+
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+ # 针对历史上发生的 <升价
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+ if not df_rise_nodes.empty:
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+ # 对准航线 航班号 行李配额
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+ df_rise_nodes_part = df_rise_nodes[
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+ (df_rise_nodes['citypair'] == city_pair) &
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+ (df_rise_nodes['flight_numbers'] == flight_numbers) &
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+ (df_rise_nodes['baggage_weight'] == baggage_weight)
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+ ]
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+ # 升价前 增量阈值、当前阈值 的匹配
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+ if not df_rise_nodes_part.empty and pd.notna(price_change_amount):
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+ pca_base_1 = float(price_change_amount)
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+ pca_vals_1 = pd.to_numeric(df_rise_nodes_part['prev_rise_change_amount'], errors='coerce')
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+ df_rise_gap_1 = df_rise_nodes_part.loc[
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+ pca_vals_1.notna(),
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+ ['rise_days_to_departure', 'rise_hours_until_departure', 'rise_price_change_percent', 'rise_price_change_amount',
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+ 'prev_rise_duration_hours', 'prev_rise_change_percent', 'prev_rise_change_amount', 'prev_rise_amount', 'relative_position']
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+ ].copy()
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+ df_rise_gap_1['pca_gap'] = (pca_vals_1.loc[pca_vals_1.notna()] - pca_base_1)
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+ df_rise_gap_1['pca_abs_gap'] = df_rise_gap_1['pca_gap'].abs()
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+
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+ price_base_1 = pd.to_numeric(price_amount, errors='coerce')
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+ rise_price_vals_1 = pd.to_numeric(df_rise_gap_1['prev_rise_amount'], errors='coerce')
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+ df_rise_gap_1['price_gap'] = rise_price_vals_1 - price_base_1
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+ df_rise_gap_1['price_abs_gap'] = df_rise_gap_1['price_gap'].abs()
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+
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+ df_rise_gap_1 = df_rise_gap_1.sort_values(['price_abs_gap', 'pca_abs_gap'], ascending=[True, True])
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+ df_match_1 = df_rise_gap_1.loc[(df_rise_gap_1['price_abs_gap'] <= 5.0) & (df_rise_gap_1['pca_abs_gap'] <= 10.0)].copy()
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+
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+ # 历史上出现的极近似的增长(下降)幅度后的升价场景
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+ if not df_match_1.empty:
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+ dur_base_1 = pd.to_numeric(price_duration_hours, errors='coerce')
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+ # hud_base_1 = pd.to_numeric(hours_until_departure, errors='coerce')
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+ dtd_base_1 = pd.to_numeric(days_to_departure, errors='coerce')
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+
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+ if pd.notna(dur_base_1) and pd.notna(dtd_base_1):
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+ df_match_chk_1 = df_match_1.copy()
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+
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+ drop_dtd_vals_1 = pd.to_numeric(df_match_chk_1['rise_days_to_departure'], errors='coerce')
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+ df_match_chk_1 = df_match_chk_1.loc[drop_dtd_vals_1.notna()].copy()
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+ df_match_chk_1 = df_match_chk_1.loc[(drop_dtd_vals_1.loc[drop_dtd_vals_1.notna()] - float(dtd_base_1)).abs() <= 3].copy()
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+
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+ # 距离起飞天数也对的上
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+ if not df_match_chk_1.empty:
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+ length_rise = df_match_chk_1.shape[0]
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+ df_min_hours.loc[idx, 'rise_price_sample_size'] = length_rise
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+
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+ rise_price_change_upper = df_match_chk_1['rise_price_change_amount'].max() # 涨价上限
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+ rise_price_change_lower = df_match_chk_1['rise_price_change_amount'].min() # 涨价下限
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+ df_min_hours.loc[idx, 'rise_price_change_upper'] = round(rise_price_change_upper, 2)
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+ df_min_hours.loc[idx, 'rise_price_change_lower'] = round(rise_price_change_lower, 2)
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+
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+ # 可以明确的判定不降价
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+ if length_drop == 0:
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+ df_min_hours.loc[idx, 'simple_will_price_drop'] = 0
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+ df_min_hours.loc[idx, 'simple_drop_in_hours'] = 0
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+ df_min_hours.loc[idx, 'simple_drop_in_hours_prob'] = 0.0
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+ # df_min_hours.loc[idx, 'simple_drop_in_hours_dist'] = 'r0'
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+ df_min_hours.loc[idx, 'flag_dist'] = 'r0'
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+ # 分歧判定
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+ else:
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+ drop_prob = round(length_drop / (length_rise + length_drop), 2)
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+ # 依旧保持之前的降价判定，概率修改
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+ if drop_prob >= 0.4:
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+ df_min_hours.loc[idx, 'simple_will_price_drop'] = 1
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+ # df_min_hours.loc[idx, 'simple_drop_in_hours_dist'] = 'd1'
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+ df_min_hours.loc[idx, 'flag_dist'] = 'd1'
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+ # 改判不降价，概率修改
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+ else:
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+ df_min_hours.loc[idx, 'simple_will_price_drop'] = 0
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+ # df_min_hours.loc[idx, 'simple_drop_in_hours_dist'] = 'r1'
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+ df_min_hours.loc[idx, 'flag_dist'] = 'r1'
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+
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+ df_min_hours.loc[idx, 'simple_drop_in_hours_prob'] = drop_prob
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+
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+ print("判定循环结束")
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+
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+ _dep_hour = pd.to_datetime(df_min_hours["from_time"], errors="coerce").dt.floor("h")
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+ df_min_hours["valid_begin_hour"] = (_dep_hour - pd.to_timedelta(360, unit="h")).dt.strftime("%Y-%m-%d %H:%M:%S")
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+ df_min_hours["valid_end_hour"] = (_dep_hour - pd.to_timedelta(24, unit="h")).dt.strftime("%Y-%m-%d %H:%M:%S")
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+
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+ # 要展示在预测表里的字段
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+ order_cols = [
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+ "citypair", "flight_numbers", "baggage_weight", "from_date", "from_time",
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+ "cabins", "ticket_amount", "currency",
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+ "price_total", 'relative_position', 'days_to_departure', 'hours_until_departure',
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+ 'price_change_amount', 'price_change_percent', 'price_duration_hours',
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+ "update_hour", "update_week",
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+ 'valid_begin_hour', 'valid_end_hour',
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+ 'simple_will_price_drop', 'simple_drop_in_hours', 'simple_drop_in_hours_prob', 'simple_drop_in_hours_dist',
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+ 'flag_dist',
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+ 'drop_price_change_upper', 'drop_price_change_lower', 'drop_price_sample_size',
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+ 'rise_price_change_upper', 'rise_price_change_lower', 'rise_price_sample_size',
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+ ]
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+ df_predict = df_min_hours[order_cols]
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+ df_predict = df_predict.rename(columns={
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+ 'simple_will_price_drop': 'will_price_drop',
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+ 'simple_drop_in_hours': 'drop_in_hours',
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+ 'simple_drop_in_hours_prob': 'drop_in_hours_prob',
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+ 'simple_drop_in_hours_dist': 'drop_in_hours_dist',
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+ }
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+ )
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+
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+ # 排序
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+ df_predict = df_predict.sort_values(
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+ by=['citypair', 'flight_numbers', 'baggage_weight', 'from_date'],
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+ kind='mergesort',
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+ na_position='last',
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+ ).reset_index(drop=True)
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+
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+ total_cnt = len(df_predict)
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+ if "will_price_drop" in df_predict.columns:
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+ _wpd = pd.to_numeric(df_predict["will_price_drop"], errors="coerce")
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+ drop_1_cnt = int((_wpd == 1).sum())
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+ drop_0_cnt = int((_wpd == 0).sum())
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+ else:
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+ drop_1_cnt = 0
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+ drop_0_cnt = 0
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+ print(f"will_price_drop 分类数量统计: 1(会降)={drop_1_cnt}, 0(不降)={drop_0_cnt}, 总数={total_cnt}")
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+
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+ csv_path1 = os.path.join(predict_dir, f'future_predictions_{pred_time_str}.csv')
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+ df_predict.to_csv(csv_path1, mode='a', index=False, header=not os.path.exists(csv_path1), encoding='utf-8-sig')
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+
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+ print("预测结果已追加")
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+ return df_predict
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node04