def regression(col_y, col_x, lag, season_tf, num_day, day_of_week, df):
# Get dataframe for col1
dates_1 = lib.generate_season_date_by_dof(season_tf, num_day, day_of_week,
df['date'])
df1 = lib.get_df_by_dates(dates_1, df)
# Get dataframe for col2
dates_2 = lib.get_dates_lag(dates_1, lag)
df2 = lib.get_df_by_dates(dates_2, df)
col1_data = df2[col_x]
col2_data = df1[col_y]
col1_data_rm, col2_data_rm = lib.remove_outlier(col1_data, col2_data)
regr = linear_model.LinearRegression()
col1_data_rm = col1_data_rm.reshape(len(col1_data_rm), 1)
try:
regr.fit(col1_data_rm, col2_data_rm)
result = {
'coef': regr.coef_[0],
'intercept': regr.intercept_,
'score': regr.score(col1_data_rm, col2_data_rm)
}
except:
print dates_1
print dates_2
print col1_data, col2_data
result = {'coef': 0, 'intercept': 0}
raise
return result
def get_df_corr_data(dep_id, seg_id, season_tf, day_of_week, df):
"""
Get data to test. It's consistent with the ways getting data in compute_corr
: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
: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'}
y_series = lib.get_df_by_dates(y_dates,df)[y_col]
df_result = pd.DataFrame({y_col:list(y_series)})
for lag in [0, 1, 7, 14, 21, 30, 365]:
for i, col in enumerate(cols):
df_result["date_{}".format(lag)] = lib.get_dates_lag(y_dates, lag)
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)
if len(col_series) < len(y_dates):
df_result["{}_{}".format(col, lag)] = list(col_series) + ['F']*(len(y_dates)-len(col_series))
else:
df_result["{}_{}".format(col, lag)] = list(col_series)
return df_result
def get_series_to_compute_corr(y_col_name, x_col_name, lag, y_dates, df):
"""
Prepare series to compute correlation
:param y_col_name: name of column y
:param x_col_name: name of column x
:param lag: lag days between y_col and x_col
:param y_dates: dates series by y_col
:param df: dataframe contain all data of one hotel
:return:
"""
col_dates = lib.get_dates_lag(y_dates, lag)
y_series = lib.get_df_by_dates(y_dates, df)[y_col_name]
col_series = lib.get_df_by_dates(col_dates, df)[x_col_name]
# if length of x_col data smaller than of y_col just return data by length x_col
return y_series[:len(col_series)], col_series
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_3_week_1(dep_id, seg_id, season_tf, significance_level, df, date_ss):
"""
Compute correlation 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_dates = date_ss
y_col = '_'.join([str(dep_id), str(seg_id)]) + '_rv' # Get revenue data name
y_series = lib.get_df_by_dates(y_dates, df)[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
for i, col in enumerate(cols):
if lib.check_total(y_col, col):
continue
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
col_dates = lib.get_dates_lag(y_dates, lag)
col_series = lib.get_df_by_dates(col_dates, df)[col]
y_series_rm, col_series = lib.remove_outlier(y_series, col_series)
cov_val = np.corrcoef(y_series_rm, col_series)[1, 0]
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_capture_arr_week(col1, col2, lag, season_tf, df, year_input):
""" neeed to change the comment later
Compute capture and arr for original column col1 and lag column col2
:param col1: dep_seg id of original column
:param col2: dep_seg id of lag column
:param lag:
:param season_tf: season time frame
:param day_of_week:
:param df: dataframe contain data of one hotel
:return: dictionary contain 'capture', 'ARR', 'capture_unit', 'ARR_unit'
"""
# Get dataframe for col1
dates_1 = lib.generate_season_date(season_tf, year_input)
df1 = lib.get_df_by_dates(dates_1, df)
# Get dataframe for col2
dates_2 = lib.get_dates_lag(dates_1, lag)
df2 = lib.get_df_by_dates(dates_2, df)
root_col1 = lib.get_root_col(col1)
root_col2 = lib.get_root_col(col2)
type_col2 = lib.get_type_col(col2)
col1_rv = "{}_rv".format(root_col1)
col1_rn = "{}_rn".format(root_col1)
col2_rv = "{}_rv".format(root_col2)
col2_rn = "{}_rn".format(root_col2)
col2_gn = "{}_gn".format(root_col2)
capture = 1
ARR = 0
capture_unit = 1
ARR_unit = 0
try:
if root_col1 == root_col2:
if col1_rn in df.columns: # exist unit ?
capture = 1
col1_rn_rm, col2_rn_rm = lib.remove_outlier(
df1[col1_rn], df2[col2_rn])
ARR = (col1_rn_rm / col2_rn_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
capture_unit = 1
ARR_unit = (col1_rn_rm / col2_rn_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
else:
capture = 1
col1_rv_rm, col2_rm = lib.remove_outlier(
df1[col1_rv], df2[col2])
ARR = (col1_rv_rm / col2_rm).replace([np.inf, -np.inf],
np.nan).dropna().median()
capture_unit = 1
ARR_unit = (col1_rv_rm / col2_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
else:
if col1_rn in df.columns: # exist unit ?
if type_col2 == 'rv':
capture = 1
col1_rv_rm, col2_rv_rm = lib.remove_outlier(
df1[col1_rv], df2[col2_rv])
ARR = (col1_rv_rm / col2_rv_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
capture_unit = 1
col1_rn_rm, col2_rv_rm = lib.remove_outlier(
df1[col1_rn], df2[col2_rv])
ARR_unit = (col1_rn_rm / col2_rv_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
elif type_col2 == 'rn':
col1_rn_rm, col2_rn_rm = lib.remove_outlier(
df1[col1_rn], df2[col2_rn])
capture = (col1_rn_rm / col2_rn_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
col1_rv_rm, col1_rn_rm = lib.remove_outlier(
df1[col1_rv], df1[col1_rn])
ARR = (col1_rv_rm / col1_rn_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
capture_unit = 1
col1_rn_rm, col2_rn_rm = lib.remove_outlier(
df1[col1_rn], df2[col2_rn])
ARR_unit = (col1_rn_rm / col2_rn_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
elif type_col2 == 'gn':
col1_rn_rm, col2_gn_rm = lib.remove_outlier(
df1[col1_rn], df2[col2_gn])
capture = (col1_rn_rm / col2_gn_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
col1_rv_rm, col1_rn_rm = lib.remove_outlier(
df1[col1_rv], df1[col1_rn])
ARR = (col1_rv_rm / col1_rn_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
capture_unit = 1
col1_rn_rm, col2_gn_rm = lib.remove_outlier(
df1[col1_rn], df2[col2_gn])
ARR_unit = (col1_rn_rm / col2_gn_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
else:
if type_col2 == 'rv':
capture = 1
col1_rm, col2_rv_rm = lib.remove_outlier(
df1[col1], df2[col2_rv])
ARR = (col1_rm / col2_rv_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
elif type_col2 == 'rn':
capture = 1
col1_rm, col2_rn_rm = lib.remove_outlier(
df1[col1], df2[col2_rn])
ARR = (col1_rm / col2_rn_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
elif type_col2 == 'gn':
capture = 1
col1_rm, col2_gn_rm = lib.remove_outlier(
df1[col1], df2[col2_gn])
ARR = (col1_rm / col2_gn_rm).replace(
[np.inf, -np.inf], np.nan).dropna().median()
except:
capture = 1
ARR = 0
capture = 0 if pd.isnull(capture) else capture
ARR = 0 if pd.isnull(ARR) else ARR
ARR_unit = 0 if pd.isnull(ARR_unit) else ARR_unit
result = {
'capture': capture,
'ARR': ARR,
'capture_unit': capture_unit,
'ARR_unit': ARR_unit
}
return result
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, 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, 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)