feat: upgraged univarhybrid, multivarpure, multivarhybrid with evalua…

…te model methods
maxmekiska · May 12, 2024 · b4fb4cd · b4fb4cd
1 parent fe5ecb7
commit b4fb4cd
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Showing 8 changed files with 78 additions and 14 deletions.
diff --git a/imbrium/predictors/multivarhybrid.py b/imbrium/predictors/multivarhybrid.py
@@ -9,7 +9,8 @@
                                           cnngru, cnnlstm, cnnrnn)
 from imbrium.blueprints.abstract_multivariate import MultiVariateMultiStep
 from imbrium.utils.optimizer import get_optimizer
-from imbrium.utils.transformer import data_prep_multi, multistep_prep_hybrid
+from imbrium.utils.transformer import (data_prep_multi, multistep_prep_hybrid,
+                                       train_test_split)
 
 
 class BaseHybridMulti(MultiVariateMultiStep):
@@ -46,6 +47,8 @@ def _model_intake_prep(
             self.input_x, self.input_y, self.modified_back = multistep_prep_hybrid(
                 temp_data, sub_seq, steps_past, steps_future
             )
+            self.input_x, self.input_x_test = train_test_split(self.input_x)
+            self.input_y, self.input_y_test = train_test_split(self.input_y)
         else:
             pass
 
@@ -750,6 +753,13 @@ def fit_model(
                 )
         return self.details
 
+    def evaluate_model(self):
+        self.evaluation_details = self.model.evaluate(
+            x=self.input_x_test, y=self.input_y_test
+        )
+
+        return self.evaluation_details
+
     def model_blueprint(self):
         """Prints a summary of the models layer structure."""
         self.model.summary()
@@ -758,6 +768,10 @@ def show_performance(self):
         """Returns performance details."""
         return self.details
 
+    def show_evaluation(self):
+        """Returns performance details on test data."""
+        return self.evaluation_details
+
     def predict(
         self,
         data: array,

diff --git a/imbrium/predictors/multivarpure.py b/imbrium/predictors/multivarpure.py
@@ -9,7 +9,9 @@
                                           mlp, rnn)
 from imbrium.blueprints.abstract_multivariate import MultiVariateMultiStep
 from imbrium.utils.optimizer import get_optimizer
-from imbrium.utils.transformer import data_prep_multi, multistep_prep_standard
+from imbrium.utils.transformer import (data_prep_multi,
+                                       multistep_prep_standard,
+                                       train_test_split)
 
 
 class BasePureMulti(MultiVariateMultiStep):
@@ -42,6 +44,8 @@ def _model_intake_prep(self, steps_past: int, steps_future: int) -> None:
             self.input_x, self.input_y = multistep_prep_standard(
                 temp_data, steps_past, steps_future
             )
+            self.input_x, self.input_x_test = train_test_split(self.input_x)
+            self.input_y, self.input_y_test = train_test_split(self.input_y)
         else:
             pass
 
@@ -161,6 +165,12 @@ def create_mlp(
 
         self.input_x = self.input_x.reshape((self.input_x.shape[0], self.dimension))
 
+        self.dimension_test = self.input_x_test.shape[1] * self.input_x_test.shape[2]
+
+        self.input_x_test = self.input_x_test.reshape(
+            (self.input_x_test.shape[0], self.dimension_test)
+        )
+
         self.model = mlp(
             optimizer=optimizer_obj,
             loss=loss,
@@ -710,6 +720,13 @@ def fit_model(
                 )
         return self.details
 
+    def evaluate_model(self):
+        self.evaluation_details = self.model.evaluate(
+            x=self.input_x_test, y=self.input_y_test
+        )
+
+        return self.evaluation_details
+
     def model_blueprint(self):
         """Prints a summary of the models layer structure."""
         self.model.summary()
@@ -718,6 +735,10 @@ def show_performance(self):
         """Returns performance details."""
         return self.details
 
+    def show_evaluation(self):
+        """Returns performance details on test data."""
+        return self.evaluation_details
+
     def predict(self, data: array) -> array:
         """Takes in a sequence of values and outputs a forecast.
         Parameters:

diff --git a/imbrium/predictors/univarhybrid.py b/imbrium/predictors/univarhybrid.py
@@ -9,7 +9,8 @@
                                           cnngru, cnnlstm, cnnrnn)
 from imbrium.blueprints.abstract_univariate import UniVariateMultiStep
 from imbrium.utils.optimizer import get_optimizer
-from imbrium.utils.transformer import data_prep_uni, sequence_prep_hybrid_uni
+from imbrium.utils.transformer import (data_prep_uni, sequence_prep_hybrid_uni,
+                                       train_test_split)
 
 
 class BaseHybridUni(UniVariateMultiStep):
@@ -42,6 +43,8 @@ def _model_intake_prep(
             self.input_x, self.input_y, self.modified_back = sequence_prep_hybrid_uni(
                 temp_data, sub_seq, steps_past, steps_future
             )
+            self.input_x, self.input_x_test = train_test_split(self.input_x)
+            self.input_y, self.input_y_test = train_test_split(self.input_y)
         else:
             pass
 
@@ -722,6 +725,13 @@ def fit_model(
                 )
         return self.details
 
+    def evaluate_model(self):
+        self.evaluation_details = self.model.evaluate(
+            x=self.input_x_test, y=self.input_y_test
+        )
+
+        return self.evaluation_details
+
     def model_blueprint(self):
         """Prints a summary of the models layer structure."""
         self.model.summary()
@@ -730,6 +740,10 @@ def show_performance(self):
         """Returns performance details."""
         return self.details
 
+    def show_evaluation(self):
+        """Returns performance details on test data."""
+        return self.evaluation_details
+
     def predict(
         self, data: array, sub_seq: int = None, steps_past=None, steps_future=None
     ) -> array:

diff --git a/imbrium/predictors/univarpure.py b/imbrium/predictors/univarpure.py
@@ -10,7 +10,9 @@
                                           mlp, rnn)
 from imbrium.blueprints.abstract_univariate import UniVariateMultiStep
 from imbrium.utils.optimizer import get_optimizer
-from imbrium.utils.transformer import data_prep_uni, sequence_prep_standard_uni
+from imbrium.utils.transformer import (data_prep_uni,
+                                       sequence_prep_standard_uni,
+                                       train_test_split)
 
 
 class BasePureUni(UniVariateMultiStep):
@@ -39,8 +41,8 @@ def _model_intake_prep(self, steps_past: int, steps_future: int) -> None:
             self.input_x, self.input_y = sequence_prep_standard_uni(
                 temp_data, steps_past, steps_future
             )
-            self.input_x, self.input_x_test = self._train_test_split(self.input_x)
-            self.input_y, self.input_y_test = self._train_test_split(self.input_y)
+            self.input_x, self.input_x_test = train_test_split(self.input_x)
+            self.input_y, self.input_y_test = train_test_split(self.input_y)
         else:
             pass
 

diff --git a/imbrium/utils/transformer.py b/imbrium/utils/transformer.py
@@ -1,4 +1,6 @@
-from numpy import array, dstack, empty, reshape, vstack
+from typing import Tuple
+
+from numpy import array, dstack, empty, vstack
 
 
 def data_prep_uni(data: array) -> array:
@@ -260,3 +262,14 @@ def multistep_prep_hybrid(
     X = dstack(X)
     Y = Y[0]  # getting array out of list
     return X, Y, mod
+
+
+def train_test_split(data: array, test_size=0.2) -> Tuple[array, array]:
+    """Splits the time series data into training and testing sets."""
+    n_samples = len(data)
+    n_test = int(test_size * n_samples)
+
+    train_data = data[:-n_test]
+    test_data = data[-n_test:]
+
+    return train_data, test_data
diff --git a/tests/test_predictors/test_multivarhybrid.py b/tests/test_predictors/test_multivarhybrid.py
@@ -148,8 +148,8 @@
 
 y = np.array([3.422, 2.697, 2.992, 2.414, 2.267])
 
-shape_x = (490, 1, 40, 1)
-shape_y = (490, 5)
+shape_x = (392, 1, 40, 1)
+shape_y = (392, 5)
 
 model_id = "CNN-LSTM"
 optimizer = "adam"
@@ -533,7 +533,7 @@ def test_create_fit_cnnlstm():
         pytest.fail(f"An exception was raised: {e}")
 
 
-def test_create_cnngru():
+def test_create_fit_cnngru():
     try:
         test1.create_fit_cnngru(sub_seq=1, steps_past=5, steps_future=5, epochs=1)
     except Exception as e:

diff --git a/tests/test_predictors/test_multivarpure.py b/tests/test_predictors/test_multivarpure.py
@@ -96,8 +96,8 @@
 
 y = np.array([3.585, 3.521, 3.413, 3.422, 2.697, 2.992, 2.414, 2.267, 2.611, 2.815])
 
-shape_x = (488, 2, 8)
-shape_y = (488, 10)
+shape_x = (391, 2, 8)
+shape_y = (391, 10)
 
 model_id = "LSTM"
 optimizer = "adam"

diff --git a/tests/test_predictors/test_univarhybrid.py b/tests/test_predictors/test_univarhybrid.py
@@ -70,8 +70,8 @@
 y = np.array([[2.135], [1.913], [1.592]])
 
 
-shape_x = (486, 2, 5, 1)
-shape_y = (486, 3, 1)
+shape_x = (389, 2, 5, 1)
+shape_y = (389, 3, 1)
 
 model_id = "CNN-LSTM"
 optimizer = "adam"