Move predictors out and into SDK, remove storing of CAO in models

use_cases/eluc/app/components/prediction.py

@@ -8,13 +8,13 @@
 from dash import html
 import pandas as pd
 
+from prsdk.persistence.persistors.hf_persistor import HuggingFacePersistor
+from prsdk.persistence.serializers.neural_network_serializer import NeuralNetSerializer
+from prsdk.persistence.serializers.sklearn_serializer import SKLearnSerializer
 from prsdk.predictors.predictor import Predictor
 
 from app import constants as app_constants
 from data import constants
-from persistence.persistors.hf_persistor import HuggingFacePersistor
-from persistence.serializers.neural_network_serializer import NeuralNetSerializer
-from persistence.serializers.sklearn_serializer import SKLearnSerializer
 from predictors.percent_change.percent_change_predictor import PercentChangePredictor
 
 

use_cases/eluc/app/components/prescription.py

@@ -9,9 +9,10 @@
 import pandas as pd
 import plotly.graph_objects as go
 
+from prsdk.persistence.persistors.hf_persistor import HuggingFacePersistor
+
 from app import constants as app_constants
 from data import constants
-from persistence.persistors.hf_persistor import HuggingFacePersistor
 from persistence.serializers.prescriptor_serializer import PrescriptorSerializer
 from prescriptors.prescriptor_manager import PrescriptorManager
 

use_cases/eluc/experiments/predictor_experiments.ipynb

@@ -26,14 +26,14 @@
     "from scipy.stats import ttest_1samp, ttest_ind\n",
     "from sklearn.metrics import mean_absolute_error\n",
     "\n",
+    "from prsdk.persistence.serializers.neural_network_serializer import NeuralNetSerializer\n",
+    "from prsdk.persistence.serializers.sklearn_serializer import SKLearnSerializer\n",
     "from prsdk.predictors.predictor import Predictor\n",
+    "from prsdk.predictors.neural_network.neural_net_predictor import NeuralNetPredictor\n",
+    "from prsdk.predictors.sklearn_predictor.sklearn_predictor import LinearRegressionPredictor, RandomForestPredictor\n",
     "\n",
     "from data.eluc_data import ELUCData\n",
-    "from data import constants\n",
-    "from persistence.serializers.neural_network_serializer import NeuralNetSerializer\n",
-    "from persistence.serializers.sklearn_serializer import SKLearnSerializer\n",
-    "from predictors.neural_network.neural_net_predictor import NeuralNetPredictor\n",
-    "from predictors.sklearn_predictor.sklearn_predictor import LinearRegressionPredictor, RandomForestPredictor"
+    "from data import constants"
    ]
   },
   {

use_cases/eluc/experiments/predictor_significance.py

@@ -10,11 +10,13 @@
 from tqdm import tqdm
 from sklearn.metrics import mean_absolute_error
 
+from prsdk.predictors.neural_network.neural_net_predictor import NeuralNetPredictor
+from prsdk.predictors.sklearn_predictors.linear_regression_predictor import LinearRegressionPredictor
+from prsdk.predictors.sklearn_predictors.random_forest_predictor import RandomForestPredictor
+
 from data.eluc_data import ELUCData
 from data import constants
 from data.conversion import construct_countries_df
-from predictors.neural_network.neural_net_predictor import NeuralNetPredictor
-from predictors.sklearn_predictor.sklearn_predictor import RandomForestPredictor, LinearRegressionPredictor
 
 
 def train_and_test(n: int,

use_cases/eluc/experiments/prescriptor_experiments.ipynb

@@ -24,14 +24,15 @@
     "import numpy as np\n",
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
+    "from prsdk.predictors.neural_network.neural_net_predictor import NeuralNetPredictor\n",
+    "\n",
     "from data import constants\n",
     "from data.eluc_data import ELUCData\n",
     "from data.eluc_encoder import ELUCEncoder\n",
     "from prescriptors.nsga2.candidate import Candidate\n",
     "from prescriptors.nsga2.land_use_prescriptor import LandUsePrescriptor\n",
     "from prescriptors.prescriptor_manager import PrescriptorManager\n",
-    "from prescriptors.heuristics.heuristics import EvenHeuristic, PerfectHeuristic\n",
-    "from predictors.neural_network.neural_net_predictor import NeuralNetPredictor"
+    "from prescriptors.heuristics.heuristics import EvenHeuristic, PerfectHeuristic"
    ]
   },
   {

use_cases/eluc/predictors/custom/template/template_predictor.py

@@ -5,20 +5,13 @@
 
 from prsdk.predictors.predictor import Predictor
 
-from data import constants
-
 
 class TemplatePredictor(Predictor):
     """
     A template predictor returning dummy values for ELUC.
     The class that gets passed into the Evaluator should call the load method which should return a Predictor.
     The Predictor just needs to impelement predict.
     """
-    def __init__(self):
-        super().__init__(context=constants.CAO_MAPPING["context"],
-                         actions=constants.CAO_MAPPING["actions"],
-                         outcomes=constants.CAO_MAPPING["outcomes"])
-
     def fit(self, X_train, y_train):
         pass