chore: added pre-commit, improved typing

.github/workflows/main.yml

@@ -19,4 +19,4 @@ jobs:
       - name: Install tox and any other packages
         run: pip install tox
       - name: Run tox
-        run: tox -e py
+        run: tox -e py

.gitignore

@@ -22,4 +22,4 @@ share/python-wheels/
 *.egg
 .tox
 env/
-.coverage
+.coverage

.pre-commit-config.yaml

@@ -0,0 +1,8 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: check-yaml
+      - id: detect-private-key
+      - id: end-of-file-fixer
+      - id: trailing-whitespace

CHANGELOG.md

@@ -89,12 +89,12 @@ activation function type
 - removed encoder-decoder architectures
 - improved layer configuration via dictionary input
 - split data argument into target and feature numpy arrays
- 
+
 ### 2.0.1
 
-- fix: removed dead pandas imports 
+- fix: removed dead pandas imports
 - chore: added tensorflow as base requirement
- 
+
 ### 2.1.0
 
 - feat!: removed data preparation out of predictor class, sub_seq, steps_past, steps_future need now to be defined in each model method
@@ -107,9 +107,12 @@ activation function type
 - 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 
-  
+- refactor!: removed validation split from `fit_model`. Control validation and test split via evaluation_split and validation_split paramters in class variables
+
 ### 3.1.0
 
 - feat: added optional `batch_size` paramter to `fit_model`
-- refactor!: train, test, validation split default change
+- feat: added Tensor Board to `evaluate_model`
+- refactor!: train, test, validation split default change
+- chore: added pre-commit checks
+- refactor: added, improved typing

README.md

@@ -1,5 +1,5 @@
 # imbrium [![Downloads](https://pepy.tech/badge/imbrium)](https://pepy.tech/project/imbrium) [![PyPi](https://img.shields.io/pypi/v/imbrium.svg?color=blue)](https://pypi.org/project/imbrium/) [![GitHub license](https://img.shields.io/github/license/maxmekiska/Imbrium?color=black)](https://github.com/maxmekiska/Imbrium/blob/main/LICENSE) [![PyPI pyversions](https://img.shields.io/pypi/pyversions/imbrium.svg)](https://pypi.python.org/project/imbrium/)
- 
+
 ## Status
 
 | Build | Status|
@@ -34,7 +34,7 @@ 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.
 
 
 ### Get started with imbrium
@@ -58,12 +58,12 @@ Note, if you choose to use TensorBoard, run the following command to display the
 from imbrium import PureUni
 
 # create a PureUni object (numpy array expected)
-predictor = PureUni(target = target_numpy_array, evaluation_split = 0.1, validation_split = 0.2) 
+predictor = PureUni(target = target_numpy_array, evaluation_split = 0.1, validation_split = 0.2)
 
 # the following models are available for a PureUni objects;
 
 # create and fit a muti-layer perceptron model
-predictor.create_fit_mlp( 
+predictor.create_fit_mlp(
         steps_past,
         steps_future,
         optimizer = "adam",
@@ -225,7 +225,7 @@ predictor = create_fit_cnn(
         board = False,
 )
 
-# create and fit a gated recurrent unit neural network  
+# create and fit a gated recurrent unit neural network
 predictor.create_fit_gru(
         steps_past,
         steps_future,
@@ -400,7 +400,7 @@ predictor.freeze(absolute_path)
 
 # load saved predictor via:
 predictor.retrieve(location)
-```  
+```
 
 </details>
 
@@ -413,12 +413,12 @@ predictor.retrieve(location)
 from imbrium import PureMulti
 
 # create a PureMulti object (numpy array expected)
-predictor = PureMulti(target = target_numpy_array, features = features_numpy_array, evaluation_split = 0.1, validation_split = 0.2) 
+predictor = PureMulti(target = target_numpy_array, features = features_numpy_array, evaluation_split = 0.1, validation_split = 0.2)
 
 # the following models are available for a PureMulti objects;
 
 # create and fit a muti-layer perceptron model
-predictor.create_fit_mlp( 
+predictor.create_fit_mlp(
         steps_past,
         steps_future,
         optimizer = "adam",
@@ -580,7 +580,7 @@ predictor = create_fit_cnn(
         board = False,
 )
 
-# create and fit a gated recurrent unit neural network  
+# create and fit a gated recurrent unit neural network
 predictor.create_fit_gru(
         steps_past,
         steps_future,
@@ -755,7 +755,7 @@ predictor.freeze(absolute_path)
 
 # load saved predictor via:
 predictor.retrieve(location)
-```  
+```
 </details>
 
 <details>
@@ -766,7 +766,7 @@ predictor.retrieve(location)
 from imbrium import HybridUni
 
 # create a HybridUni object (numpy array expected)
-predictor = HybridUni(target = target_numpy_array, evaluation_split = 0.1, validation_split = 0.2) 
+predictor = HybridUni(target = target_numpy_array, evaluation_split = 0.1, validation_split = 0.2)
 
 # the following models are available for a HybridUni objects:
 # create and fit a convolutional recurrent neural network
@@ -887,7 +887,7 @@ predictor.create_fit_cnnlstm(
         board = False,
 )
 
-# create and fit a convolutional gated recurrent unit neural network  
+# create and fit a convolutional gated recurrent unit neural network
 predictor.create_fit_cnngru(
         sub_seq,
         steps_past,
@@ -1158,7 +1158,7 @@ predictor.freeze(absolute_path)
 
 # load saved predictor via:
 predictor.retrieve(location)
-```  
+```
 
 </details>
 
@@ -1171,7 +1171,7 @@ predictor.retrieve(location)
 from imbrium import HybridMulti
 
 # create a HybridMulti object (numpy array expected)
-predictor = HybridMulti(target = target_numpy_array, features = features_numpy_array, evaluation_split = 0.1, validation_split = 0.2) 
+predictor = HybridMulti(target = target_numpy_array, features = features_numpy_array, evaluation_split = 0.1, validation_split = 0.2)
 
 # the following models are available for a HybridMulti objects:
 # create and fit a convolutional recurrent neural network
@@ -1292,7 +1292,7 @@ predictor.create_fit_cnnlstm(
         board = False,
 )
 
-# create and fit a convolutional gated recurrent unit neural network  
+# create and fit a convolutional gated recurrent unit neural network
 predictor.create_fit_cnngru(
         sub_seq,
         steps_past,
@@ -1563,7 +1563,7 @@ predictor.freeze(absolute_path)
 
 # load saved predictor via:
 predictor.retrieve(location)
-```  
+```
 </details>
 
 </details>
@@ -1602,4 +1602,4 @@ perceptron-models-for-time-series-forecasting/.
 </details>
 
 
-</details>
+</details>

imbrium/blueprints/abstract_multivariate.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 
-from numpy import array
+import numpy as np
 
 
 class MultiVariateMultiStep(ABC):
@@ -11,8 +11,8 @@ class MultiVariateMultiStep(ABC):
     @abstractmethod
     def __init__(
         self,
-        target: array = array([]),
-        features: array = array([]),
+        target: np.ndarray = np.array([]),
+        features: np.ndarray = np.array([]),
         evaluation_split: float = 0.2,
         validation_split: float = 0.2,
     ):
@@ -27,12 +27,12 @@ def set_model_id(self, name: str):
 
     @property
     @abstractmethod
-    def get_target(self) -> array:
+    def get_target(self) -> np.ndarray:
         pass
 
     @property
     @abstractmethod
-    def get_target_shape(self) -> array:
+    def get_target_shape(self) -> np.ndarray:
         pass
 
     @property
@@ -42,7 +42,7 @@ def get_model_id(self) -> str:
 
     @property
     @abstractmethod
-    def get_X_input(self) -> array:
+    def get_X_input(self) -> np.ndarray:
         pass
 
     @property
@@ -52,7 +52,7 @@ def get_X_input_shape(self) -> tuple:
 
     @property
     @abstractmethod
-    def get_y_input(self) -> array:
+    def get_y_input(self) -> np.ndarray:
         pass
 
     @property
@@ -79,14 +79,16 @@ def get_metrics(self) -> str:
     def fit_model(
         self,
         epochs: int,
+        board: bool = False,
+        batch_size=None,
         show_progress: int = 1,
         validation_split: float = 0.20,
         **callback_setting: dict
     ):
         pass
 
     @abstractmethod
-    def evaluate_model(self):
+    def evaluate_model(self, board: bool = False):
         pass
 
     @abstractmethod
@@ -104,11 +106,11 @@ def show_evaluation(self):
     @abstractmethod
     def predict(
         self,
-        data: array,
+        data: np.ndarray,
         sub_seq: int = None,
         steps_past: int = None,
         steps_future: int = None,
-    ) -> array:
+    ) -> np.ndarray:
         pass
 
     @abstractmethod

imbrium/blueprints/abstract_univariate.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 
-from numpy import array
+import numpy as np
 
 
 class UniVariateMultiStep(ABC):
@@ -11,8 +11,8 @@ class UniVariateMultiStep(ABC):
     @abstractmethod
     def __init__(
         self,
-        target: array = array([]),
-        features: array = array([]),
+        target: np.ndarray = np.array([]),
+        features: np.ndarray = np.array([]),
         evaluation_split: float = 0.2,
         validation_split: float = 0.2,
     ):
@@ -27,12 +27,12 @@ def set_model_id(self, name: str):
 
     @property
     @abstractmethod
-    def get_target(self) -> array:
+    def get_target(self) -> np.ndarray:
         pass
 
     @property
     @abstractmethod
-    def get_target_shape(self) -> array:
+    def get_target_shape(self) -> np.ndarray:
         pass
 
     @property
@@ -42,7 +42,7 @@ def get_model_id(self) -> str:
 
     @property
     @abstractmethod
-    def get_X_input(self) -> array:
+    def get_X_input(self) -> np.ndarray:
         pass
 
     @property
@@ -52,7 +52,7 @@ def get_X_input_shape(self) -> tuple:
 
     @property
     @abstractmethod
-    def get_y_input(self) -> array:
+    def get_y_input(self) -> np.ndarray:
         pass
 
     @property
@@ -79,14 +79,16 @@ def get_metrics(self) -> str:
     def fit_model(
         self,
         epochs: int,
+        board: bool = False,
+        batch_size=None,
         show_progress: int = 1,
         validation_split: float = 0.20,
         **callback_setting: dict
     ):
         pass
 
     @abstractmethod
-    def evaluate_model(self):
+    def evaluate_model(self, board: bool = False):
         pass
 
     @abstractmethod
@@ -102,7 +104,7 @@ def show_evaluation(self):
         pass
 
     @abstractmethod
-    def predict(self, data: array) -> array:
+    def predict(self, data: np.ndarray) -> np.ndarray:
         pass
 
     @abstractmethod