Merge pull request #10 from maxmekiska/development

imbrium 3.0.0

Author: maxmekiska

.github/workflows/main.yml

@@ -8,7 +8,7 @@ jobs:
     runs-on: windows-latest
     strategy:
       matrix:
-        python: ["3.8", "3.9", "3.10", "3.11"]
+        python: ["3.9", "3.10", "3.11"]
 
     steps:
       - uses: actions/checkout@v3

CHANGELOG.md

@@ -84,7 +84,7 @@ activation function type
 
 ### 2.0.0
 
-- adapted `keras_core`
+- adapted `keras`
 - removed internal hyperparameter tuning
 - removed encoder-decoder architectures
 - improved layer configuration via dictionary input
@@ -99,4 +99,12 @@ activation function type
 
 - feat!: removed data preparation out of predictor class, sub_seq, steps_past, steps_future need now to be defined in each model method
   - allows for advanced hyper parameter tuning
-- fix: removed tensor board activation logic bug
+- fix: removed tensor board activation logic bug
+
+### 3.0.0
+
+- chore!: changed from temp library keras_core to keras > 3.0.0
+- chore!: removed python 3.8 support to accomodate tensorflow and keras dependiencies
+- chore: increased major to 3.0.0 to align with keras major
+- feat: added evaluate_model method to test model performance on test data
+- refactor!: removed validation split from `fit_model`. Control validation and test split via evaluation_split and validation_split paramters in class variables 

README.md

@@ -34,9 +34,9 @@ imbrium is a deep learning library that specializes in time series forecasting.
 
 imbrium is designed to simplify the application of deep learning models for time series forecasting. The library offers a variety of pre-built architectures. The user retains full control over the configuration of each layer, including the number of neurons, the type of activation function, loss function, optimizer, and metrics applied. This allows for the flexibility to adapt the architecture to the specific needs of the forecast task at hand. Imbrium also offers a user-friendly interface for training and evaluating these models, making it easy to quickly iterate and test different configurations.
 
-imbrium uses the sliding window approach to generate forecasts. The sliding window approach in time series forecasting involves moving a fixed-size window (steps_past) through historical data, using the data within the window as input features. The next data points outside the window are used as the target variables (steps_future). This method allows the model to learn sequential patterns and trends in the data, enabling accurate predictions for future points in the time series. 
+imbrium uses the sliding window approach to generate forecasts. The sliding window approach in time series forecasting involves moving a fixed-size window - `steps_past` through historical data, using the data within the window as input features. The next data points outside the window are used as the target variables - `steps_future`. This method allows the model to learn sequential patterns and trends in the data, enabling accurate predictions for future points in the time series. 
 
-## imbrium 2.0.0
+## imbrium `2.0.0`
 
 - adapting `keras_core`
 - removing internal hyperparameter tuning
@@ -45,6 +45,10 @@ imbrium uses the sliding window approach to generate forecasts. The sliding wind
 - split input data into target and feature numpy arrays
 - overall lighten the library
 
+## imbrium `3.0.0`
+
+- switch from `keras_core` to `keras > 3.0.0`
+
 ### Get started with imbrium
 
 <details>
@@ -60,7 +64,7 @@ imbrium uses the sliding window approach to generate forecasts. The sliding wind
 from imbrium import PureUni
 
 # create a PureUni object (numpy array expected)
-predictor = PureUni(target = target_numpy_array) 
+predictor = PureUni(target = target_numpy_array, evaluation_split = 0.2, validation_split = 0.2) 
 
 # the following models are available for a PureUni objects;
 
@@ -98,7 +102,6 @@ predictor.create_fit_mlp(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -136,7 +139,6 @@ predictor.create_fit_rnn(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -174,7 +176,6 @@ predictor.create_fit_lstm(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -223,7 +224,6 @@ predictor = create_fit_cnn(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -265,7 +265,6 @@ predictor.create_fit_gru(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -298,7 +297,6 @@ predictor.create_fit_birnn(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -331,7 +329,6 @@ predictor.create_fit_bilstm(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -364,7 +361,6 @@ predictor.create_fit_bigru(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -385,6 +381,12 @@ start_from_epoch=0
 # instpect model structure
 predictor.model_blueprint()
 
+# evaluate model performance on testing data
+predictor.evaluate_model()
+
+# get model performance on testing data
+predictor.show_evaluation()
+
 # insptect keras model performances via (access dictionary via history key):
 predictor.show_performance()
 
@@ -409,7 +411,7 @@ predictor.retrieve(location)
 from imbrium import PureMulti
 
 # create a PureMulti object (numpy array expected)
-predictor = PureMulti(target = target_numpy_array, features = features_numpy_array) 
+predictor = PureMulti(target = target_numpy_array, features = features_numpy_array, evaluation_split = 0.2, validation_split = 0.2) 
 
 # the following models are available for a PureMulti objects;
 
@@ -447,7 +449,6 @@ predictor.create_fit_mlp(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -485,7 +486,6 @@ predictor.create_fit_rnn(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -523,7 +523,6 @@ predictor.create_fit_lstm(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -572,7 +571,6 @@ predictor = create_fit_cnn(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -614,7 +612,6 @@ predictor.create_fit_gru(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -647,7 +644,6 @@ predictor.create_fit_birnn(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -680,7 +676,6 @@ predictor.create_fit_bilstm(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -713,7 +708,6 @@ predictor.create_fit_bigru(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -734,6 +728,12 @@ start_from_epoch=0
 # instpect model structure
 predictor.model_blueprint()
 
+# evaluate model performance on testing data
+predictor.evaluate_model()
+
+# get model performance on testing data
+predictor.show_evaluation()
+
 # insptect keras model performances via (access dictionary via history key):
 predictor.show_performance()
 
@@ -756,7 +756,7 @@ predictor.retrieve(location)
 from imbrium import HybridUni
 
 # create a HybridUni object (numpy array expected)
-predictor = HybridUni(target = target_numpy_array) 
+predictor = HybridUni(target = target_numpy_array, evaluation_split = 0.2, validation_split = 0.2) 
 
 # the following models are available for a HybridUni objects:
 # create and fit a convolutional recurrent neural network
@@ -814,7 +814,6 @@ predictor.create_fit_cnnrnn(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -873,7 +872,6 @@ predictor.create_fit_cnnlstm(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -932,7 +930,6 @@ predictor.create_fit_cnngru(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -991,7 +988,6 @@ predictor.create_fit_cnnbirnn(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -1050,7 +1046,6 @@ predictor.create_fit_cnnbilstm(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -1109,7 +1104,6 @@ predictor.create_fit_cnnbigru(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -1130,6 +1124,12 @@ start_from_epoch=0
 # instpect model structure
 predictor.model_blueprint()
 
+# evaluate model performance on testing data
+predictor.evaluate_model()
+
+# get model performance on testing data
+predictor.show_evaluation()
+
 # insptect keras model performances via (access dictionary via history key):
 predictor.show_performance()
 
@@ -1155,7 +1155,7 @@ predictor.retrieve(location)
 from imbrium import HybridMulti
 
 # create a HybridMulti object (numpy array expected)
-predictor = HybridMulti(target = target_numpy_array, features = features_numpy_array) 
+predictor = HybridMulti(target = target_numpy_array, features = features_numpy_array, evaluation_split = 0.2, validation_split = 0.2) 
 
 # the following models are available for a HybridMulti objects:
 # create and fit a convolutional recurrent neural network
@@ -1213,7 +1213,6 @@ predictor.create_fit_cnnrnn(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -1272,7 +1271,6 @@ predictor.create_fit_cnnlstm(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -1331,7 +1329,6 @@ predictor.create_fit_cnngru(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -1390,7 +1387,6 @@ predictor.create_fit_cnnbirnn(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -1449,7 +1445,6 @@ predictor.create_fit_cnnbilstm(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -1508,7 +1503,6 @@ predictor.create_fit_cnnbigru(
         },
         epochs = 100,
         show_progress = 1,
-        validation_split = 0.20,
         board = False,
 )
 
@@ -1529,6 +1523,12 @@ start_from_epoch=0
 # instpect model structure
 predictor.model_blueprint()
 
+# evaluate model performance on testing data
+predictor.evaluate_model()
+
+# get model performance on testing data
+predictor.show_evaluation()
+
 # insptect keras model performances via (access dictionary via history key):
 predictor.show_performance()
 
@@ -1555,7 +1555,7 @@ https://github.com/maxmekiska/ImbriumTesting-Demo/blob/main/use-case-1.ipynb
 https://github.com/maxmekiska/ImbriumTesting-Demo/blob/main/IntegrationTest.ipynb
 
 
-## LEGACY: imbrium versions <= v.1.3.0
+## legacy: imbrium versions `<= 1.3.0`
 <details>
   <summary>Expand</summary>
   <br>

imbrium/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "2.1.0"
+__version__ = "3.0.0"
 
 from imbrium.predictors.multivarhybrid import HybridMulti
 from imbrium.predictors.multivarpure import PureMulti