def compute_corr_ver_2_week_1(dep_id, seg_id, season_tf, significance_level,
df, year_input, date_ss):
y_dates = date_ss
y_col = '_'.join([str(dep_id), str(seg_id)
]) + '_rv' # Get revenue data name
df1_full = lib.get_df_by_dates(y_dates, df)
y_series = df1_full[y_col]
cols = set(df.columns) - {'date', 'day_of_week'}
result_df = pd.DataFrame(columns=[
'col', "cov_col", 'lag', 'cov_value', "conf_interval_0",
"conf_interval_1", "adj_corr_col"
])
# x is some col with some lag
index = 0
dict_df = {}
dict_df["0_2"] = df1_full[df1_full.columns]
dict_df["0_1"] = df1_full[df1_full.columns]
for lag in [1, 7, 365]:
col_dates = lib.get_dates_lag(y_dates, lag)
df2 = lib.get_df_by_dates(col_dates, df)
dict_df[str(lag) + "_2"] = df2[df2.columns]
dict_df[str(lag) + "_1"] = df1_full[(len(df1_full) - len(df2)):]
for i, col in enumerate(cols):
if lib.check_total(y_col, col):
continue
for lag in [0, 1, 7, 365]:
if (col.split('_')[0] != str(dep_id)) and (lag not in [0, 1]):
continue
if (col.split('_')[0] == str(dep_id)) and (lag in [0, 1]):
continue
df1 = dict_df[str(lag) + "_1"]
df2 = dict_df[str(lag) + "_2"]
col_series = df2[col]
y_series_rm = y_series[-len(col_series):]
y_series_rm, col_series = lib.remove_outlier(
y_series_rm, col_series)
cap_arr = compute_capture_arr_ver_2(y_col, col, 1, 1, df1, df2,
cols)
cov_val = 1 - lib.compute_error(y_series_rm, col_series,
cap_arr['capture'], cap_arr['ARR'])
#===========================
if lag == 365:
y_365_lag = df2[col]
date_y_365_lag_not_null = df2[~y_365_lag.isnull()]['date']
if len(date_y_365_lag_not_null.dt.year.unique()) < 3:
cov_val = -99 # explaination is big
#===========================
if "rn" in col:
conf_interval_0, conf_interval_1 = cov_val, cov_val
else:
conf_interval_0, conf_interval_1 = lib.compute_interval(
cov_val, significance_level)
result_df.loc[index] = [
y_col, col, lag, cov_val, conf_interval_0, conf_interval_1,
col + "_" + str(lag)
]
index += 1
result_df = result_df.fillna(-99)
return result_df.sort_values('cov_value', ascending=False)
def compute_corr_ver_2(dep_id, seg_id, season_tf, num_day, day_of_week,
significance_level, df):
"""
Compute explanation (1 -error) for all pair of deparment_segment in one hotel
:param dep_id: department id
:param seg_id: segment id
:param season_tf: season timeframe
:param day_of_week: day of week range 0-6
:param df: Dataframe contain all data of one hotel
:param significance_level: significance to compute confidence interval
:return:
"""
dates = df['date'].sort_values(ascending=False)
season_dates = pd.Series(lib.get_season_dates_from_tf(season_tf, dates))
y_dates = list(season_dates[season_dates.dt.weekday == day_of_week])
y_col = '_'.join([str(dep_id), str(seg_id)
]) + '_rv' # Get revenue data name
cols = set(df.columns) - {'date', 'day_of_week'}
result_df = pd.DataFrame(columns=[
'col', "cov_col", 'lag', 'cov_value', "conf_interval_0",
"conf_interval_1", "adj_corr_col"
])
# x is some col with some lag
index = 0
for i, col in enumerate(cols):
for lag in [0, 1, 7, 14, 21, 30, 365]:
if (col.split('_')[0] != str(dep_id)) and (lag not in [0, 1]):
continue
if (col.split('_')[0] == str(dep_id)) and (lag in [0, 1]):
continue
y_series, col_series = get_series_to_compute_corr(
y_col, col, lag, y_dates, df)
cap_arr = compute_capture_arr(y_col, col, lag, season_tf, num_day,
day_of_week, df)
y_series_rm, col_series = lib.remove_outlier(y_series, col_series)
cov_val = 1 - lib.compute_error(y_series_rm, col_series,
cap_arr['capture'], cap_arr['ARR'])
if "rn" in col:
conf_interval_0, conf_interval_1 = cov_val, cov_val
else:
conf_interval_0, conf_interval_1 = lib.compute_interval(
cov_val, significance_level)
result_df.loc[index] = [
y_col, col, lag, cov_val, conf_interval_0, conf_interval_1,
col + "_" + str(lag)
]
index += 1
result_df = result_df.fillna(-99)
return result_df.sort_values('cov_value', ascending=False)
def compute_corr_ver_2_1_cruise(dep_id, seg_id, season_tf, num_day,
day_of_week, significance_level, df, date_ss,
cruise):
"""
Compute explanation (1 -error) for all pair of deparment_segment in one hotel
:param dep_id: department id
:param seg_id: segment id
:param season_tf: season timeframe
:param day_of_week: day of week range 0-6
:param df: Dataframe contain all data of one hotel
:param significance_level: significance to compute confidence interval
:return:
"""
y_weekdays = cruise.cruiseday_list(date_ss)
y_dates_full = [
date_ss[i] for i, d in enumerate(y_weekdays) if d == day_of_week
]
y_dates = y_dates_full[-(min(num_day, len(y_dates_full))):]
y_col = '_'.join([str(dep_id), str(seg_id)
]) + '_rv' # Get revenue data name
df1_full = lib.get_df_by_dates(y_dates_full, df)
y_series = df1_full[y_col]
df1 = lib.get_df_by_dates(y_dates, df1_full)
cols = set(df.columns) - {'date', 'day_of_week'}
result_df = pd.DataFrame(columns=[
'col', "cov_col", 'lag', 'cov_value', "conf_interval_0",
"conf_interval_1", "adj_corr_col"
])
# x is some col with some lag
index = 0
dict_df = {}
dict_df[0] = df1[df1.columns]
dict_df["0_full"] = df1_full[df1_full.columns]
for lag in [1, 7]: #remove lag 365
dates = lib.get_dates_lag(y_dates, lag)
dates_full = lib.get_dates_lag(y_dates_full, lag)
df2_full = lib.get_df_by_dates(dates_full, df)
df2 = lib.get_df_by_dates(dates, df2_full)
dict_df[lag] = df2
dict_df[str(lag) + "_full"] = df2_full
for i, col in enumerate(cols):
if lib.check_total(y_col, col):
continue
for lag in [0, 1, 7]: #remove lag 365
if (col.split('_')[0] != str(dep_id)) and (lag not in [0, 1]):
continue
if (col.split('_')[0] == str(dep_id)) and (lag in [0, 1]):
continue
col_series = dict_df[str(lag) + "_full"][col]
y_series_r = y_series[-len(col_series):]
df2 = dict_df[lag]
y_series_rm, col_series_rm = lib.remove_outlier(
y_series_r, col_series)
cap_arr = compute_capture_arr_ver_2(y_col, col, 1, 1, df1, df2,
cols)
cov_val = 1 - lib.compute_error(y_series_rm, col_series_rm,
cap_arr['capture'], cap_arr['ARR'])
if y_col == "69_107_rv" and day_of_week == 6 and season_tf == [
('01-01', '02-17'), ('12-23', '12-31')
] and lag == 0:
print('test', col)
with open(
'test_caset_69_107_{0}_{1}.txt'.format(season_tf, col),
'w') as f:
f.write("\ny_series: {0}\ncol_series:{1}\ny_rm:{2}\ncol_rm: {3}\ncap_arr: {4}\cov_val:{5}".format( \
", ".join(map(str,y_series_r)), ", ".join(map(str, col_series)), ", ".join(map(str, y_series_rm)), ", ".join(map(str, col_series_rm)), str(cap_arr), cov_val))
#===========================
if lag == 365:
y_365_lag = df2[col]
date_y_365_lag_not_null = df2[~y_365_lag.isnull()]['date']
if len(date_y_365_lag_not_null.dt.year.unique()) < 3:
cov_val = -99 # explaination is big
#===========================
if "rn" in col:
conf_interval_0, conf_interval_1 = cov_val, cov_val
else:
conf_interval_0, conf_interval_1 = lib.compute_interval(
cov_val, significance_level)
result_df.loc[index] = [
y_col, col, lag, cov_val, conf_interval_0, conf_interval_1,
col + "_" + str(lag)
]
index += 1
result_df = result_df.fillna(-99)
return result_df.sort_values('cov_value', ascending=False)