Standardize threshold naming

glue/flagging_script_glue/flagging.py

@@ -21,7 +21,7 @@ def go(
     iso_forest_cols: list,
     dev_bounds: tuple,
     condos: bool,
-    raw_price_thresh: int,
+    raw_price_threshold: int,
 ):
     """
     This function runs all of our other functions in the correct sequence.
@@ -52,7 +52,7 @@ def go(
     df = iso_forest(df, groups, iso_forest_cols)
     print("iso_forest() done")
     df = outlier_taxonomy(
-        df, dev_bounds, groups, condos=condos, raw_price_thresh=raw_price_thresh
+        df, dev_bounds, groups, condos=condos, raw_price_threshold=raw_price_threshold
     )
     print("outlier_taxonomy() done\nfinished")
 
@@ -73,7 +73,11 @@ def create_group_string(groups: tuple, sep: str) -> str:
 
 
 def outlier_taxonomy(
-    df: pd.DataFrame, permut: tuple, groups: tuple, condos: bool, raw_price_thresh: int
+    df: pd.DataFrame,
+    permut: tuple,
+    groups: tuple,
+    condos: bool,
+    raw_price_threshold: int,
 ):
     """
     Creates columns having to do with our chosen outlier taxonomy.
@@ -89,7 +93,7 @@ def outlier_taxonomy(
 
     df = check_days(df, SHORT_TERM_OWNER_THRESHOLD)
     df = pricing_info(df, permut, groups, condos=condos)
-    df = outlier_type(df, condos=condos, raw_price_thresh=raw_price_thresh)
+    df = outlier_type(df, condos=condos, raw_price_threshold=raw_price_threshold)
 
     return df
 
@@ -745,7 +749,9 @@ def z_normalize_groupby(s: pd.Series):
     return zscore(s, nan_policy="omit")
 
 
-def outlier_type(df: pd.DataFrame, condos: bool, raw_price_thresh: int) -> pd.DataFrame:
+def outlier_type(
+    df: pd.DataFrame, condos: bool, raw_price_threshold: int
+) -> pd.DataFrame:
     """
     This function create indicator columns for each distinct outlier type between price
     and characteristic outliers. These columns are prefixed with 'sv_ind_'.
@@ -813,7 +819,7 @@ def outlier_type(df: pd.DataFrame, condos: bool, raw_price_thresh: int) -> pd.Da
         ]
 
     # Implement raw threshold, unlog  price
-    price_conditions.append((10 ** df["meta_sale_price"]) > raw_price_thresh)
+    price_conditions.append((10 ** df["meta_sale_price"]) > raw_price_threshold)
     price_labels.append("sv_ind_raw_price_threshold")
 
     combined_conditions = price_conditions + char_conditions

glue/sales_val_flagging.py

@@ -480,9 +480,9 @@ def get_parameter_df(
     ptax_sd,
     rolling_window,
     time_frame,
-    short_term_thresh,
-    min_group_thresh,
-    raw_price_thresh,
+    short_term_threshold,
+    min_group_threshold,
+    raw_price_threshold,
     run_id,
 ):
     """
@@ -523,9 +523,9 @@ def get_parameter_df(
         "ptax_sd": [ptax_sd],
         "rolling_window": [rolling_window],
         "time_frame": [time_frame],
-        "short_term_owner_threshold": [short_term_thresh],
-        "min_group_thresh": [min_group_thresh],
-        "raw_price_thresh": [raw_price_thresh],
+        "short_term_owner_threshold": [short_term_threshold],
+        "min_group_thresh": [min_group_threshold],
+        "raw_price_threshold": [raw_price_threshold],
     }
 
     df_parameters = pd.DataFrame(parameter_dict_to_df)

manual_flagging/flagging.py

@@ -300,6 +300,7 @@ def create_bins_and_labels(input_list):
         iso_forest_cols=df_info["iso_forest_cols"],
         dev_bounds=tuple(inputs["dev_bounds"]),
         condos=df_info["condos_boolean"],
+        raw_price_threshold=inputs["raw_price_threshold"],
     )
 
     # Add the edited or unedited dataframe to the new dictionary
@@ -400,8 +401,9 @@ def create_bins_and_labels(input_list):
     ptax_sd=inputs["ptax_sd"],
     rolling_window=inputs["rolling_window_months"],
     time_frame=inputs["time_frame"],
-    short_term_thresh=flg_model.SHORT_TERM_OWNER_THRESHOLD,
+    short_term_threshold=flg_model.SHORT_TERM_OWNER_THRESHOLD,
     min_group_thresh=inputs["min_groups_threshold"],
+    raw_price_threshold=inputs["raw_price_threshold"],
     run_id=run_id,
 )