Set up intervention

conf/base/parameters.yml

@@ -382,27 +382,27 @@ models:
             flavor: DataSet
             config:
                 features:
-                    - 'no_show_before'
+                    # - 'no_show_before'
                     - 'appts_before'
-                    - 'show_before'
+                    # - 'show_before'
                     - 'no_show_rate'
                     - 'sched_days_advanced'
-                    - 'month'
+                    # - 'month'
                     - 'age'
                     - 'modality'
                     - 'occupation'
                     - 'reason'
-                    - 'sex'
+                    # - 'sex'
                     - 'hour_sched'
                     - 'distance_to_usz'
                     - 'day_of_week_str'
                     - 'marital'
                     - 'times_rescheduled'
                 target: NoShow
         Stratifier:
-            flavor: PartitionedLabelStratifier
+            flavor: PartitionedFeatureStratifier
             config:
-                n_partitions: 5
+                split_feature: 'year'
         Architecture:
             flavor: Pipeline
             config:
@@ -417,14 +417,14 @@ models:
                                 with_mean: True
                             args:
                                 columns:
-                                  - 'no_show_before'
+                                  # - 'no_show_before'
                                   - 'sched_days_advanced'
                                   - 'age'
                                   - 'hour_sched'
                                   - 'distance_to_usz'
                                   - 'times_rescheduled'
                                   - 'appts_before'
-                                  - 'show_before'
+                                  # - 'show_before'
                                   - 'no_show_rate'
                           - name: 'onehot'
                             flavor: sklearn.preprocessing.OneHotEncoder
@@ -437,13 +437,13 @@ models:
                                   - 'reason'
                                   - 'modality'
                                   - 'day_of_week_str'
-                          - name: 'cyc'
-                            flavor: mridle.utilities.modeling.CyclicalTransformer
-                            config:
-                                period: 12
-                            args:
-                                columns:
-                                    - 'month'
+                          #- name: 'cyc'
+                          #  flavor: mridle.utilities.modeling.CyclicalTransformer
+                          #  config:
+                          #      period: 12
+                          #  args:
+                          #      columns:
+                          #          - 'month'
                   - flavor: XGBClassifier
                     name: 'classifier'
                     config:

src/mridle/experiment/architecture.py

@@ -2,7 +2,7 @@
 
 import skorch
 from sklearn.base import BaseEstimator
-from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import Pipeline
 from sklearn.compose import ColumnTransformer
@@ -34,6 +34,7 @@ class ArchitectureInterface(ComponentInterface):
 
     registered_flavors = {
         'RandomForestClassifier': RandomForestClassifier,  # TODO enable auto-loading from sklearn
+        'RandomForestRegressor': RandomForestRegressor,  # TODO enable auto-loading from sklearn
         'LogisticRegression': LogisticRegression,
         'XGBClassifier': xgb.XGBClassifier,
         'Pipeline': Pipeline,

src/mridle/experiment/dataset.py

@@ -36,8 +36,13 @@ def validate_config(config, data):
             if col not in data.columns:
                 raise ValueError(f'Feature column {col} not found in dataset.')
 
-        if config['target'] not in data.columns:
-            raise ValueError(f"Target column {config['target']} not found in dataset.")
+        if isinstance(config['target'], str):
+            if config['target'] not in data.columns:
+                raise ValueError(f"Target column {config['target']} not found in dataset.")
+        elif isinstance(config['target'], list):
+            if not set(config['target']).issubset(data.columns):
+                not_in_list = list(set(config['target']).difference(data.columns))
+                raise ValueError(f"Target columns {not_in_list} not found in dataset.")
 
         return True
 

src/mridle/experiment/metric.py

@@ -135,6 +135,10 @@ class MetricInterface(ComponentInterface):
         'AUPRC': AUPRC,
         'AUROC': AUROC,
         'LogLoss': LogLoss,
+        'MAE': MAE,
+        'MSE': MSE,
+        'RMSE': RMSE,
+        'MedianAbsoluteError': MedianAbsoluteError
     }
 
     @classmethod

src/mridle/experiment/stratifier.py

@@ -58,7 +58,7 @@ def materialize_partition(self, partition_id: int, data_set: DataSet) -> Tuple[p
 class PartitionedLabelStratifier(Stratifier):
 
     def partition_data(self, data_set: DataSet) -> List[Tuple[List[int], List[int]]]:
-        """Randomly shuffle and split the doc_list into n_partitions roughly equal lists, stratified by label."""
+        """Randomly shuffle and split the data_set into n_partitions roughly equal lists, stratified by label."""
         label_list = data_set.y
         skf = StratifiedKFold(n_splits=self.config['n_partitions'], random_state=42, shuffle=True)
         x = np.zeros(len(label_list))  # split takes a X argument for backwards compatibility and is not used
@@ -100,9 +100,33 @@ def validate_config(cls, config):
         return True
 
 
+class PartitionedFeatureStratifier(Stratifier):
+
+    def partition_data(self, data_set: DataSet) -> List[Tuple[List[int], List[int]]]:
+        """Split dataset by feature values of provided column."""
+        data_set_copy = data_set.data.copy()
+        data_set_copy = data_set_copy.reset_index()
+        label_list = data_set_copy[self.config['split_feature']].unique()
+        partitions = []
+        for l_id, f_label in enumerate(label_list):
+            print(f_label)
+            train_ids = np.array(data_set_copy[data_set_copy[self.config['split_feature']] != f_label].index)
+            test_ids = np.array(data_set_copy[data_set_copy[self.config['split_feature']] == f_label].index)
+            partitions.append([train_ids, test_ids])
+        return partitions
+
+    @classmethod
+    def validate_config(cls, config):
+        for key in ['split_feature', ]:
+            if key not in config:
+                raise ValueError(f"{cls.__name__} config must contain entry '{key}'.")
+        return True
+
+
 class StratifierInterface(ComponentInterface):
 
     registered_flavors = {
+        'PartitionedFeatureStratifier': PartitionedFeatureStratifier,
         'PartitionedLabelStratifier': PartitionedLabelStratifier,
         'TrainTestStratifier': TrainTestStratifier,
     }

src/mridle/pipelines/data_science/feature_engineering/nodes.py

@@ -15,6 +15,13 @@ def daterange(date1, date2):
         yield date1 + timedelta(n)
 
 
+def get_last_non_na(x):
+    if x.last_valid_index() is None:
+        return '0'
+    else:
+        return x[x.last_valid_index()]
+
+
 def generate_training_data(status_df, valid_date_range, append_outcome=True, add_no_show_before=True):
     """
     Build data for use in models by trying to replicate the conditions under which the model would be used in reality
@@ -201,13 +208,15 @@ def build_feature_set(status_df: pd.DataFrame, valid_date_range: List[str], mast
         'distance_to_usz_sq': 'last',
         'close_to_usz': 'last',
         'times_rescheduled': 'last',
-        'start_time': 'last'
+        'start_time': 'last',
+        'Telefon': lambda x: get_last_non_na(x)
     }
 
     slot_df = build_slot_df(status_df, valid_date_range, agg_dict, build_future_slots=build_future_slots,
                             include_id_cols=True)
 
     slot_df = feature_days_scheduled_in_advance(status_df, slot_df)
+    slot_df = feature_year(slot_df)
     slot_df = feature_month(slot_df)
     slot_df = feature_hour_sched(slot_df)
     slot_df = feature_day_of_week(slot_df)
@@ -217,7 +226,7 @@ def build_feature_set(status_df: pd.DataFrame, valid_date_range: List[str], mast
     slot_df = feature_cyclical_month(slot_df)
     slot_df = slot_df[slot_df['day_of_week_str'].isin(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'])]
     slot_df = slot_df[slot_df['sched_days_advanced'] > 2]
-
+    slot_df = limit_to_day_hours(slot_df)
     return slot_df
 
 
@@ -274,6 +283,20 @@ def feature_month(slot_df: pd.DataFrame) -> pd.DataFrame:
     return slot_df
 
 
+def feature_year(slot_df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Append the year feature to the dataframe.
+
+    Args:
+        slot_df: A dataframe containing appointment slots.
+
+    Returns: A row-per-status-change dataframe with additional column 'year'.
+
+    """
+    slot_df['year'] = slot_df['start_time'].dt.year
+    return slot_df
+
+
 def feature_hour_sched(slot_df: pd.DataFrame) -> pd.DataFrame:
     """
     Append the hour_sched feature to the dataframe using was_sched_for_date.
@@ -628,10 +651,6 @@ def feature_occupation(df):
 
     df_remap.loc[df_remap['Beruf'] == 'nan', 'occupation'] = 'none_given'
     df_remap.loc[df_remap['Beruf'] == '-', 'occupation'] = 'none_given'
-    df_remap.loc[df_remap['Beruf'].apply(regex_search, search_str='rentner|Renter|pensioniert|pens.|rente'),
-                 'occupation'] = 'retired'
-    df_remap.loc[df_remap['Beruf'].apply(regex_search, search_str='keine Angaben|keine Ang'),
-                 'occupation'] = 'none_given'
     df_remap.loc[df_remap['Beruf'].apply(regex_search,
                                          search_str='Angestellte|ang.|baue|angest.|Hauswart|dozent|designer|^KV$|'
                                                     'masseu|Raumpflegerin|Apothekerin|Ing.|fotog|Psycholog|'
@@ -649,14 +668,24 @@ def feature_occupation(df):
                                                     'ingenieur|Kauf|mitarbeiter|Verkäufer|Informatiker|koch|'
                                                     'lehrer|arbeiter|architekt'),
                  'occupation'] = 'employed'
+    df_remap.loc[df_remap['Beruf'].apply(regex_search, search_str='rentner|Renter|pensioniert|pens.|rente'),
+                 'occupation'] = 'retired'
+    df_remap.loc[df_remap['Beruf'].apply(regex_search, search_str='IV-Rentner'),
+                 'occupation'] = 'iv_retired'
+
+    df_remap.loc[df_remap['Beruf'].apply(regex_search, search_str='keine Angaben|keine Ang'),
+                 'occupation'] = 'none_given'
+
     df_remap.loc[df_remap['Beruf'].apply(regex_search, search_str='student|Schüler|Doktorand|'
                                                                   'Kind|Stud.|Ausbildung|^MA$'),
                  'occupation'] = 'student'
     df_remap.loc[df_remap['Beruf'].apply(regex_search, search_str='^IV$|^IV-Bezüger|^$|arbeitslos|ohne Arbeit|'
                                                                   'ohne|o.A.|nicht Arbeitstätig|'
                                                                   'Sozialhilfeempfänger|o. Arbeit|keine Arbeit|'
-                                                                  'Asyl|RAV|Hausfrau|Hausmann'),
+                                                                  'Asyl|RAV'),
                  'occupation'] = 'unemployed'
+    df_remap.loc[
+        df_remap['Beruf'].apply(regex_search, search_str='Hausfrau|Hausmann'), 'occupation'] = 'stay_at_home_parent'
     df_remap.loc[df_remap['Beruf'].apply(regex_search, search_str='selbst'), 'occupation'] = 'self_employed'
     df_remap.loc[df_remap['Beruf'].apply(regex_search, search_str='arzt|aerzt|ärzt|pflegefachfrau|Pflegehelfer|'
                                                                   'MTRA|Erzieherin|Fachfrau Betreuung|'
@@ -667,7 +696,9 @@ def feature_occupation(df):
 
     df_remap.loc[df_remap['occupation'] == '', 'occupation'] = 'other'
     df_remap.loc[df_remap['occupation'].isna(), 'occupation'] = 'other'
+
     df_remap = df_remap.drop('Beruf', axis=1)
+
     return df_remap
 
 
@@ -715,5 +746,9 @@ def feature_duration(dicom_df: pd.DataFrame) -> pd.DataFrame:
     return dicom_df
 
 
+def limit_to_day_hours(df):
+    return df[(df['hour_sched'] < 18) & (df['hour_sched'] > 6)]
+
+
 def regex_search(x, search_str):
     return bool(re.search(search_str, x, re.IGNORECASE))

src/mridle/pipelines/data_science/live_data/nodes.py

@@ -8,7 +8,7 @@ def get_slt_with_outcome():
         '/data/mridle/data/silent_live_test/live_files/all/out_features_data/features_master_slt_features.csv',
         parse_dates=['start_time', 'end_time'])
     preds.drop(columns=['NoShow'], inplace=True)
-    actuals = pd.read_csv('/data/mridle/data/silent_live_test/live_files/all/actuals/master_actuals_with_filename.csv',
+    actuals = pd.read_csv('/data/mridle/data/silent_live_test/live_files/all/actuals/master_actuals.csv',
                           parse_dates=['start_time', 'end_time'])
 
     preds['MRNCmpdId'] = preds['MRNCmpdId'].astype(str)