Rjf/incremental inference

.gitignore

@@ -3,6 +3,7 @@
 *.swp
 *debug.log
 .DS_Store
+.vscode
 
 CFC_WebApp/config.json
 CFC_WebApp/keys.json

bin/build_label_model.py

@@ -5,13 +5,13 @@
 
 import argparse
 import uuid
-import copy
 
 import emission.pipeline.reset as epr
 import emission.core.get_database as edb
 import emission.core.wrapper.user as ecwu
 import emission.storage.timeseries.abstract_timeseries as esta
-import emission.analysis.modelling.tour_model_first_only.build_save_model as eamtb
+import emission.analysis.modelling.trip_model.run_model as eamur
+import emission.analysis.modelling.trip_model.config as eamtc
 
 def _get_user_list(args):
     if args.all:
@@ -64,4 +64,9 @@ def _email_2_user_list(email_list):
     logging.info("received list with %s users" % user_list)
     for user_id in user_list:
         logging.info("building model for user %s" % user_id)
-        eamtb.build_user_model(user_id)
+        # these can come from the application config as default values
+
+        model_type = eamtc.get_model_type()
+        model_storage = eamtc.get_model_storage()
+        min_trips = eamtc.get_minimum_trips()
+        eamur.update_trip_model(user_id, model_type, model_storage, min_trips)

conf/analysis/trip_model.conf.json.sample

@@ -0,0 +1,13 @@
+{
+    "model_type": "greedy",
+    "model_storage": "document_database",
+    "minimum_trips": 14,
+    "model_parameters": {
+        "greedy": {
+            "metric": "od_similarity",
+            "similarity_threshold_meters": 500,
+            "apply_cutoff": false,
+            "incremental_evaluation": false
+        }
+    }
+}

emission/analysis/classification/inference/labels/inferrers.py

@@ -6,6 +6,8 @@
 import copy
 
 import emission.analysis.modelling.tour_model_first_only.load_predict as lp
+import emission.analysis.modelling.trip_model.run_model as eamur
+import emission.analysis.modelling.trip_model.config as eamtc
 
 # A set of placeholder predictors to allow pipeline development without a real inference algorithm.
 # For the moment, the system is configured to work with two labels, "mode_confirm" and
@@ -140,7 +142,10 @@ def n_to_confidence_coeff(n, max_confidence=None, first_confidence=None, confide
 
 # predict_two_stage_bin_cluster but with the above reduction in confidence
 def predict_cluster_confidence_discounting(trip, max_confidence=None, first_confidence=None, confidence_multiplier=None):
-    labels, n = lp.predict_labels_with_n(trip)
+    # load application config 
+    model_type = eamtc.get_model_type()
+    model_storage = eamtc.get_model_storage()
+    labels, n = eamur.predict_labels_with_n(trip, model_type, model_storage)
     if n <= 0:  # No model data or trip didn't match a cluster
         logging.debug(f"In predict_cluster_confidence_discounting: n={n}; returning as-is")
         return labels

emission/analysis/modelling/similarity/confirmed_trip_feature_extraction.py

@@ -0,0 +1,64 @@
+from typing import List
+import emission.core.wrapper.confirmedtrip as ecwc
+
+
+def origin_features(trip: ecwc.Confirmedtrip) -> List[float]:
+    """extract the trip origin coordinates.
+
+    :param trip: trip to extract features from
+    :return: origin coordinates
+    """
+    try:
+        origin = trip['data']['start_loc']["coordinates"]
+        return origin
+    except KeyError as e:
+        msg = 'Confirmedtrip expected to have path data.start_loc.coordinates'
+        raise KeyError(msg) from e
+
+def destination_features(trip: ecwc.Confirmedtrip) -> List[float]:
+    """extract the trip destination coordinates. 
+
+    :param trip: trip to extract features from
+    :return: destination coordinates
+    """
+    try:
+        destination = trip['data']['start_loc']["coordinates"]
+        return destination
+    except KeyError as e:
+        msg = 'Confirmedtrip expected to have path data.start_loc.coordinates'
+        raise KeyError(msg) from e
+
+
+def od_features(trip: ecwc.Confirmedtrip) -> List[float]:
+    """extract both origin and destination coordinates.
+
+    :param trip: trip to extract features from
+    :return: od coordinates
+    """
+    o_lat, o_lon = origin_features(trip)
+    d_lat, d_lon = destination_features(trip)
+    return [o_lat, o_lon, d_lat, d_lon]
+
+def distance_feature(trip: ecwc.Confirmedtrip) -> List[float]:
+    """provided for forward compatibility.
+
+    :param trip: trip to extract features from
+    :return: distance feature
+    """
+    try:
+        return [trip['data']['distance']]
+    except KeyError as e:
+        msg = 'Confirmedtrip expected to have path data.distance'
+        raise KeyError(msg) from e
+
+def duration_feature(trip: ecwc.Confirmedtrip) -> List[float]:
+    """provided for forward compatibility.
+
+    :param trip: trip to extract features from
+    :return: duration feature
+    """
+    try:
+        return [trip['data']['duration']]
+    except KeyError as e:
+        msg = 'Confirmedtrip expected to have path data.duration'
+        raise KeyError(msg) from e

emission/analysis/modelling/similarity/od_similarity.py

@@ -0,0 +1,21 @@
+from typing import List
+import emission.analysis.modelling.similarity.similarity_metric as eamss
+import emission.analysis.modelling.similarity.confirmed_trip_feature_extraction as ctfe
+import emission.core.wrapper.confirmedtrip as ecwc
+import emission.core.common as ecc
+
+
+class OriginDestinationSimilarity(eamss.SimilarityMetric):
+    """
+    similarity metric which compares, for two trips, 
+    the distance for origin to origin, and destination to destination,
+    in meters.
+    """
+
+    def extract_features(self, trip: ecwc.Confirmedtrip) -> List[float]:
+        return ctfe.od_features(trip)
+
+    def similarity(self, a: List[float], b: List[float]) -> List[float]:
+        o_dist = ecc.calDistance([a[0], a[1]], [b[0], b[1]])
+        d_dist = ecc.calDistance([a[2], a[3]], [b[2], b[3]])
+        return [o_dist, d_dist]

emission/analysis/modelling/similarity/similarity_metric.py

@@ -0,0 +1,41 @@
+from abc import ABCMeta, abstractmethod
+from typing import List
+import logging
+
+import emission.core.wrapper.confirmedtrip as ecwc
+
+
+class SimilarityMetric(metaclass=ABCMeta):
+
+    @abstractmethod
+    def extract_features(self, trip: ecwc.Confirmedtrip) -> List[float]:
+        """extracts the features we want to compare for similarity
+
+        :param trip: a confirmed trip
+        :return: the features to compare
+        """
+        pass
+
+    @abstractmethod
+    def similarity(self, a: List[float], b: List[float]) -> List[float]:
+        """compares the features, producing their similarity
+        as computed by this similarity metric
+
+        :param a: features for a trip
+        :param b: features for another trip
+        :return: for each feature, the similarity of these features
+        """
+        pass
+
+    def similar(self, a: List[float], b: List[float], thresh: float) -> bool:
+        """compares the features, returning true if they are similar
+        within some threshold
+
+        :param a: features for a trip
+        :param b: features for another trip
+        :param thresh: threshold for similarity
+        :return: true if the feature similarity is within some threshold
+        """
+        similarity_values = self.similarity(a, b)
+        is_similar = all(map(lambda sim: sim <= thresh, similarity_values))
+        return is_similar

emission/analysis/modelling/similarity/similarity_metric_type.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+import enum
+
+
+import emission.analysis.modelling.similarity.od_similarity as eamso
+import emission.analysis.modelling.similarity.similarity_metric as eamss
+
+class SimilarityMetricType(enum.Enum):
+    OD_SIMILARITY = 0
+
+    def build(self) -> eamss.SimilarityMetric:
+        """
+
+        hey YOU! add future similarity metric types here please!
+
+        :raises KeyError: if the SimilarityMetricType isn't found in the below dictionary
+        :return: the associated similarity metric
+        """
+        metrics = {
+            SimilarityMetricType.OD_SIMILARITY: eamso.OriginDestinationSimilarity()
+        }
+
+        metric = metrics.get(self)
+        if metric is None:
+            names = "{" + ",".join(SimilarityMetricType.names) + "}"
+            msg = f"unknown metric type {metric}, must be one of {names}"
+            raise KeyError(msg)
+        else:
+            return metric
+
+
+    @classmethod
+    def names(cls):
+        return list(map(lambda e: e.name, list(cls)))
+
+    @classmethod
+    def from_str(cls, str):
+        """attempts to match the provided string to a known SimilarityMetricType.
+        not case sensitive.
+
+        :param str: a string name of a SimilarityMetricType
+        """
+        try:
+            str_caps = str.upper()
+            return cls[str_caps]
+        except KeyError:
+            names = "{" + ",".join(cls.names) + "}"
+            msg = f"{str} is not a known SimilarityMetricType, must be one of {names}"
+            raise KeyError(msg)

emission/analysis/modelling/tour_model/label_processing.py

@@ -34,14 +34,14 @@ def map_labels_mode(user_input_df):
     # convert mode
     if "replaced_mode" in user_input_df.columns:
         same_mode_df = user_input_df[user_input_df.replaced_mode == "same_mode"]
-        logging.debug("The following rows will be changed %s" %
-            same_mode_df.index)
-        for a in range(len(user_input_df)):
-            if user_input_df.iloc[a]["replaced_mode"] == "same_mode":
-                # to see which row will be converted
-                # logging.debug("The following rows will be changed: %s", user_input_df.iloc[a])
-                user_input_df.iloc[a]["replaced_mode"] = user_input_df.iloc[a]['mode_confirm']
-        logging.debug("Finished changing all rows")
+        if len(same_mode_df) > 0:
+            logging.debug("The following rows will be changed %s" % same_mode_df.index)
+            for a in range(len(user_input_df)):
+                if user_input_df.iloc[a]["replaced_mode"] == "same_mode":
+                    # to see which row will be converted
+                    # logging.debug("The following rows will be changed: %s", user_input_df.iloc[a])
+                    user_input_df.iloc[a]["replaced_mode"] = user_input_df.iloc[a]['mode_confirm']
+            logging.debug("Finished changing all rows")
     else:
         logging.info("map_labels_mode: no replaced mode column found, early return")
     return user_input_df

emission/analysis/modelling/trip_model/config.py

@@ -0,0 +1,79 @@
+import json
+import re
+from this import d
+from typing import Optional
+import logging
+from numpy import isin
+
+import emission.analysis.modelling.trip_model.model_storage as eamums
+import emission.analysis.modelling.trip_model.model_type as eamumt
+
+config_filename = ""
+
+def load_config():
+    global config_filename
+    try:
+        config_filename = 'conf/analysis/trip_model.conf.json'
+        config_file = open(config_filename)
+    except:
+        print("analysis.trip_model.conf.json not configured, falling back to sample, default configuration")
+        config_filename = 'conf/analysis/trip_model.conf.json.sample'
+        config_file = open('conf/analysis/trip_model.conf.json.sample')
+    ret_val = json.load(config_file)
+    config_file.close()
+    return ret_val
+
+config_data = load_config()
+
+def reload_config():
+    global config_data
+    config_data = load_config()
+
+def get_config():
+    return config_data
+
+def get_optional_config_value(key) -> Optional[str]:
+    """
+    get a config value at the provided path/key
+
+    :param key: a key name or a dot-delimited path to some key within the config object
+    :return: the value at the key, or, None if not found
+    """
+    cursor = config_data
+    path = key.split(".")
+    for k in path:
+        cursor = cursor.get(k)
+        if cursor is None:
+            return None
+    return cursor
+
+def get_config_value_or_raise(key):
+    logging.debug(f'getting key {key} in config')
+    value = get_optional_config_value(key)
+    if value is None:
+        logging.debug('config object:')
+        logging.debug(json.dumps(config_data, indent=2))
+        msg = f"expected config key {key} not found in config file {config_filename}"
+        raise KeyError(msg)
+    else:
+        return value
+
+def get_model_type():
+    model_type_str = get_config_value_or_raise('model_type')
+    model_type = eamumt.ModelType.from_str(model_type_str)
+    return model_type
+
+def get_model_storage():
+    model_storage_str = get_config_value_or_raise('model_storage')
+    model_storage = eamums.ModelStorage.from_str(model_storage_str)
+    return model_storage
+
+def get_minimum_trips():
+    minimum_trips = get_config_value_or_raise('minimum_trips')
+    if not isinstance(minimum_trips, int):
+        msg = f"config key 'minimum_trips' not an integer in config file {config_filename}"
+        raise TypeError(msg)
+    return minimum_trips
+
+
+