Refactor ml code + Facility package in scenariogen

matsim/scenariogen/data/__init__.py

@@ -4,7 +4,7 @@
 __all__ = ["read_all", "ParkingPosition", "HouseholdType", "EconomicStatus", "Gender", "Employment", "Availability",
            "Purpose",
            "TripMode", "DistanceGroup", "DurationGroup", "SourceDestinationGroup",
-           "Household", "Person", "Trip", "Activity"]
+           "Household", "Person", "Trip", "Activity", "Visitations"]
 
 import os
 from dataclasses import dataclass
@@ -119,6 +119,7 @@ def sort_idx(cls, series):
         v = list(cls)
         return series.map(v.index)
 
+
 def _df_to_categorical(df, clazz):
     """ Convert columns to categorical types """
 
@@ -378,3 +379,12 @@ class Activity:
     leg_mode: TripMode
     location: str = pd.NA
     zone: str = pd.NA
+
+
+@dataclass
+class Visitations:
+    """ Aggregated visitation information """
+    location: str
+    n: int
+    purpose: Purpose = pd.NA
+    time: pd.Timestamp = pd.NA

matsim/scenariogen/data/formats/netcheck.py

@@ -0,0 +1,52 @@
+# -*- coding: utf-8 -*-
+
+import os
+
+import pandas as pd
+
+from .. import *
+
+
+def read_visitations(folder):
+    """ Read all visits from folder """
+
+    visits = []
+
+    for f in os.listdir(folder):
+
+        if not f.endswith(".csv"):
+            continue
+
+        print("Reading", f)
+
+        t = pd.read_csv(os.path.join(folder, f), parse_dates=[0])
+        t.timestamps = t.timestamps.str.split(",")
+        t["idx"] = t.index
+
+        t = t.explode("timestamps")
+        t.timestamps = pd.to_datetime(t.timestamps, format="%H:%M:%S")
+        delta = (t.timestamps.dt.hour * 60 + t.timestamps.dt.minute) * 60 + t.timestamps.dt.second
+        delta = pd.to_timedelta(delta, unit="s")
+
+        t["ts"] = t.day + delta
+
+        t = t.groupby(["device_id", "osm_id", "idx"]).agg(start=("ts", "min"), end=("ts", "max"),
+                                                          home=("distance_to_home", "mean"),
+                                                          work=("distance_to_work", "mean")).reset_index()
+
+        t["purpose"] = Purpose.OTHER
+        t.loc[t.home == 0, "purpose"] = Purpose.HOME
+        t.loc[t.work == 0, "purpose"] = Purpose.WORK
+
+        visits.append(t)
+
+    df = pd.concat(visits)
+
+    aggr = df.groupby(["osm_id", "purpose"]).agg(count=("idx", "count")).reset_index()
+
+    total = []
+
+    for v in aggr.itertuples():
+        total.append(Visitations(v.osm_id, v.count, v.purpose))
+
+    return pd.DataFrame(total)

matsim/scenariogen/ml/__init__.py

@@ -0,0 +1,5 @@
+# -*- coding: utf-8 -*-
+
+__all__ = ['MLRegressor']
+
+from .train import MLRegressor