Enables bias adjusted wind direction

api/app/jobs/common_model_fetchers.py

@@ -14,7 +14,7 @@
                                         delete_weather_station_model_predictions,
                                         refresh_morecast2_materialized_view)
 from app.weather_models.machine_learning import StationMachineLearning
-from app.weather_models import ModelEnum, construct_interpolated_noon_prediction
+from app.weather_models import SCALAR_MODEL_VALUE_KEYS, ModelEnum, construct_interpolated_noon_prediction
 from app.schemas.stations import WeatherStation
 from app import config, configure_logging
 import app.utils.time as time_utils
@@ -362,7 +362,8 @@
             if (prev_prediction is not None
                     and prev_prediction.prediction_timestamp.hour == 18
                     and prediction.prediction_timestamp.hour == 21):
-                noon_prediction = construct_interpolated_noon_prediction(prev_prediction, prediction)
+                noon_prediction = construct_interpolated_noon_prediction(
+                    prev_prediction, prediction, SCALAR_MODEL_VALUE_KEYS)
                 self._process_prediction(
                     noon_prediction, station, model_run, machine)
             self._process_prediction(

api/app/tests/weather_models/crud.py

@@ -14,58 +14,68 @@ def get_actuals_left_outer_join_with_predictions(*args):
             weather_date=datetime(2020, 10, 10, 18),
             temperature=20,
             temp_valid=True,
+            wind_direction=90,
             relative_humidity=50,
             rh_valid=True),
-            ModelRunPrediction(
+         ModelRunPrediction(
             tmp_tgl_2=2,
             rh_tgl_2=10,
             apcp_sfc_0=2,
+            wdir_tgl_10=97,
             prediction_timestamp=datetime(2020, 10, 10, 18))],
         [HourlyActual(weather_date=datetime(2020, 10, 10, 19)), None],
         [HourlyActual(weather_date=datetime(2020, 10, 10, 20),
                       temperature=25,
+                      wind_direction=270,
                       temp_valid=True,
                       relative_humidity=70,
                       rh_valid=True), None],
         [HourlyActual(
             weather_date=datetime(2020, 10, 10, 21),
             temperature=30,
             temp_valid=True,
+            wind_direction=120,
             relative_humidity=100,
             rh_valid=True),
             ModelRunPrediction(
             tmp_tgl_2=1,
             rh_tgl_2=20,
             apcp_sfc_0=3,
+            wdir_tgl_10=101,
             prediction_timestamp=datetime(2020, 10, 10, 21))],
         # day 2
         [HourlyActual(
             weather_date=datetime(2020, 10, 11, 18),
             temperature=20,
             temp_valid=True,
+            wind_direction=121,
             relative_humidity=50,
             rh_valid=True),
             ModelRunPrediction(
             tmp_tgl_2=2,
             rh_tgl_2=10,
             apcp_sfc_0=2,
+            wdir_tgl_10=110,
             prediction_timestamp=datetime(2020, 10, 11, 18))],
         [HourlyActual(weather_date=datetime(2020, 10, 11, 19)), None],
         [HourlyActual(weather_date=datetime(2020, 10, 11, 20),
                       temperature=27,
                       temp_valid=True,
+                      wind_direction=98,
                       relative_humidity=60,
                       rh_valid=True), None],
         [HourlyActual(
             weather_date=datetime(2020, 10, 11, 21),
             temperature=30,
+            wind_direction=118,
             temp_valid=True,
             relative_humidity=100,
             rh_valid=True),
             ModelRunPrediction(
             tmp_tgl_2=1,
             rh_tgl_2=20,
             apcp_sfc_0=3,
+            wdir_tgl_10=111,
             prediction_timestamp=datetime(2020, 10, 11, 21))]
     ]
     return result

api/app/weather_models/__init__.py

@@ -109,7 +109,8 @@ def interpolate_wind_direction(prediction_a: ModelRunPrediction,
 
 
 def construct_interpolated_noon_prediction(prediction_a: ModelRunPrediction,
-                                           prediction_b: ModelRunPrediction):
+                                           prediction_b: ModelRunPrediction,
+                                           model_keys):
     """ Construct a noon prediction by interpolating.
     """
     # create a noon prediction. (using utc hour 20, as that is solar noon in B.C.)
@@ -121,7 +122,7 @@ def construct_interpolated_noon_prediction(prediction_a: ModelRunPrediction,
     timestamp_b = prediction_b.prediction_timestamp.timestamp()
     noon_timestamp = noon_prediction.prediction_timestamp.timestamp()
     # calculate interpolated values.
-    for key in SCALAR_MODEL_VALUE_KEYS:
+    for key in model_keys:
         value_a = getattr(prediction_a, key)
         value_b = getattr(prediction_b, key)
         if value_a is None or value_b is None:

api/app/weather_models/fetch/predictions.py

@@ -164,7 +164,7 @@ async def fetch_latest_model_run_predictions_by_station_code_and_date_range(sess
                     temperature=bias_adjusted_temp,
                     relative_humidity=bias_adjusted_rh,
                     wind_speed=bias_adjusted_wind_speed,
-                    wind_dir=bias_adjusted_wdir
+                    wind_direction=bias_adjusted_wdir
                 ))
     return post_process_fetched_predictions(results)
 

api/app/weather_models/machine_learning.py

@@ -12,12 +12,14 @@
 from app.db.models.weather_models import (PredictionModel, ModelRunPrediction)
 from app.db.models.observations import HourlyActual
 from app.db.crud.observations import get_actuals_left_outer_join_with_predictions
+from app.weather_models.regression_model import RegressionModelsV2
+from app.weather_models.sample import Samples
 
 
 logger = getLogger(__name__)
 
 # Corresponding key values on HourlyActual and SampleCollection
-SAMPLE_VALUE_KEYS = ('temperature', 'relative_humidity', 'wind_speed', 'wind_direction')
+SAMPLE_VALUE_KEYS = ('temperature', 'relative_humidity', 'wind_speed')
 # Number of days of historical actual data to learn from when training model
 MAX_DAYS_TO_LEARN = 19
 
@@ -38,59 +40,12 @@
     """
 
     keys = ('temperature_wrapper', 'relative_humidity_wrapper',
-            'wind_speed_wrapper', 'wind_direction_wrapper')
+            'wind_speed_wrapper')
 
     def __init__(self):
         self.temperature_wrapper = LinearRegressionWrapper()
         self.relative_humidity_wrapper = LinearRegressionWrapper()
         self.wind_speed_wrapper = LinearRegressionWrapper()
-        self.wind_direction_wrapper = LinearRegressionWrapper()
-
-
-class Samples:
-    """ Class for storing samples in buckets of hours.
-    e.g. a temperature sample consists of an x axis (predicted values) and a y axis (observed values) put
-    together in hour buckets.
-    """
-
-    def __init__(self):
-        self._x = defaultdict(list)
-        self._y = defaultdict(list)
-
-    def hours(self):
-        """ Return all the hours used to bucket samples together. """
-        return self._x.keys()
-
-    def append_x(self, value, timestamp: datetime):
-        """ Append another predicted value. """
-        self._x[timestamp.hour].append(value)
-
-    def append_y(self, value, timestamp: datetime):
-        """ Append another observered values. """
-        self._y[timestamp.hour].append(value)
-
-    def np_x(self, hour):
-        """ Return numpy array of the predicted values, reshaped appropriately. """
-        return np.array(self._x[hour]).reshape((-1, 1))
-
-    def np_y(self, hour):
-        """ Return a numpy array of the observed values """
-        return np.array(self._y[hour])
-
-    def add_sample(self,
-                   model_value: float,
-                   actual_value: float,
-                   timestamp: datetime,
-                   model_key: str,
-                   sample_key: str):
-        """ Add a sample, interpolating the model values spatially """
-        # Additional logging to assist with finding errors:
-        logger.info('adding sample for %s->%s with: model_values %s, actual_value: %s',
-                    model_key, sample_key, model_value, actual_value)
-        # Add to the data we're going to learn from:
-        # Using two variables, the interpolated temperature value, and the hour of the day.
-        self.append_x(model_value, timestamp)
-        self.append_y(actual_value, timestamp)
 
 
 class SampleCollection:
@@ -100,7 +55,6 @@
         self.temperature = Samples()
         self.relative_humidity = Samples()
         self.wind_speed = Samples()
-        self.wind_direction = Samples()
 
 
 class StationMachineLearning:
@@ -126,6 +80,7 @@
         self.target_coordinate = target_coordinate
         self.station_code = station_code
         self.regression_models = defaultdict(RegressionModels)
+        self.regression_models_v2 = RegressionModelsV2()
         self.max_learn_date = max_learn_date
         # Maximum number of days to try to learn from. Experimentation has shown that
         # about two weeks worth of data starts giving fairly good results compared to human forecasters.
@@ -152,12 +107,9 @@
                 # are None.
                 logger.warning('no model value for %s->%s', model_key, sample_key)
 
-    def _collect_data(self):
+    def _collect_data(self, start_date: datetime):
         """ Collect data to use for machine learning.
         """
-        # Calculate the date to start learning from.
-        start_date = self.max_learn_date - \
-            timedelta(days=self.max_days_to_learn)
         # Create a convenient structure to store samples in.
         sample_collection = SampleCollection()
 
@@ -177,7 +129,8 @@
                         and prev_prediction.prediction_timestamp.hour == 18):
                     # If there's a gap in the data (like with the GLOBAL model) - then make up
                     # a noon prediction using interpolation, and add it as a sample.
-                    noon_prediction = construct_interpolated_noon_prediction(prev_prediction, prediction)
+                    noon_prediction = construct_interpolated_noon_prediction(
+                        prev_prediction, prediction, SCALAR_MODEL_VALUE_KEYS)
                     self._add_sample_to_collection(
                         noon_prediction, prev_actual, sample_collection)
 
@@ -190,8 +143,12 @@
     def learn(self):
         """ Collect data and perform linear regression.
         """
+        # Calculate the date to start learning from.
+        start_date = self.max_learn_date - \
+            timedelta(days=self.max_days_to_learn)
+
         # collect data
-        data = self._collect_data()
+        data = self._collect_data(start_date)
 
         # iterate through the data, creating a regression model for each variable
         # and each hour.
@@ -206,6 +163,12 @@
                 # how much sample data we actually had etc., and then not mark the model as being "good".
                 regression_model.good_model = True
 
+        # wdir specific using new structure for regression handling
+        query = get_actuals_left_outer_join_with_predictions(
+            self.session, self.model.id, self.station_code, start_date, self.max_learn_date)
+        self.regression_models_v2.collect_data(query)
+        self.regression_models_v2.train()
+
     def predict_temperature(self, model_temperature: float, timestamp: datetime):
         """ Predict the bias adjusted temperature for a given point in time, given a corresponding model
         temperature.
@@ -255,9 +218,7 @@
         : return: The bias-adjusted wind direction as predicted by the linear regression model.
         """
         hour = timestamp.hour
-        if self.regression_models[hour].wind_direction_wrapper.good_model and model_wind_dir is not None:
-            predicted_wind_dir = self.regression_models[hour].wind_direction_wrapper.model.predict([[model_wind_dir]])[
-                0]
-            # a valid wind direction value is between 0 and 360. If the returned value is outside these bounds, correct it
-            return predicted_wind_dir % 360
-        return None
+        predicted_wind_dir = self.regression_models_v2._models[0].predict(hour, [[model_wind_dir]])
+        if predicted_wind_dir is None:
+            return None
+        return predicted_wind_dir % 360