feat!: substitute GraphMode enum with string in API

chemfusekit/__base.py

@@ -92,8 +92,15 @@ def __getitem__(self, index):
 class BaseSettings:
     """Holds the settings for all objects with settings."""
 
-    def __init__(self, output: GraphMode = GraphMode.NONE):
-        self.output = output
+    def __init__(self, output: str = 'none'):
+        if output == 'none':
+            self.output = GraphMode.NONE
+        elif output == 'graphical':
+            self.output = GraphMode.GRAPHIC
+        elif output == 'text':
+            self.output = GraphMode.TEXT
+        else:
+            raise ValueError("The output mode should be 'none', 'graphical' or 'text'.")
 
 
 class BaseActionClass(ABC):
@@ -145,9 +152,9 @@ def export_model(self, export_path: str):
 class BaseClassifierSettings(BaseSettings):
     """Holds the settings for the BaseClassifier object."""
 
-    def __init__(self, output: GraphMode = GraphMode.NONE, test_split: bool = False):
+    def __init__(self, output: str = 'none', test_split: bool = False):
         super().__init__(output)
-        if test_split is True and output is GraphMode.NONE:
+        if test_split is True and self.output is GraphMode.NONE:
             raise Warning(
                 "You selected test_split but it won't run because you disabled the output."
             )

chemfusekit/df.py

@@ -7,7 +7,8 @@
 
 import matplotlib.pyplot as plt
 
-from .__base import GraphMode, BaseDataModel, BaseSettings
+from .__base import BaseDataModel, BaseSettings
+from .__utils import GraphMode
 from .pca import PCASettings, PCA
 from .plsda import PLSDASettings, PLSDA
 
@@ -37,7 +38,7 @@ def __init__(self, x_data: pd.DataFrame, x_train: pd.DataFrame, y: np.ndarray,
 class DFSettings(BaseSettings):
     """Holds the settings for the DF object."""
 
-    def __init__(self, output: GraphMode = GraphMode.NONE, method: str = 'concat'):
+    def __init__(self, output: str = 'none', method: str = 'concat'):
         super().__init__(output)
         if method not in ['concat', 'outer']:
             raise ValueError("DF: invalid method: must be 'concat' or 'outer'")

chemfusekit/knn.py

@@ -11,7 +11,7 @@
 class KNNSettings(BaseClassifierSettings):
     """Holds the settings for the kNN object."""
     def __init__(self, n_neighbors: int = 15, metric: str | Callable = 'euclidean', weights: str | Callable = 'uniform',
-                 algorithm: str = 'auto', output: GraphMode = GraphMode.NONE, test_split: bool = False):
+                 algorithm: str = 'auto', output: str = 'none', test_split: bool = False):
 
         super().__init__(output, test_split)
 
@@ -68,7 +68,7 @@ def train(self):
             self.settings.output
         )
 
-        if self.settings.test_split and self.settings.output:
+        if self.settings.test_split and self.settings.output is not GraphMode.NONE:
             knn_split = KNeighborsClassifier(
                 n_neighbors=self.settings.n_neighbors,
                 metric=self.settings.metric,

chemfusekit/lda.py

@@ -9,8 +9,8 @@
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LD
 from sklearn.model_selection import cross_val_score
 
-from chemfusekit.__utils import graph_output, run_split_test
-from chemfusekit.__utils import print_confusion_matrix, print_table, GraphMode
+from chemfusekit.__utils import graph_output, run_split_test, GraphMode
+from chemfusekit.__utils import print_confusion_matrix, print_table
 from .__base import BaseDataModel, BaseClassifier, BaseClassifierSettings, BaseReducer, ReducerDataModel
 
 
@@ -25,7 +25,7 @@ def __init__(self, x_data: pd.DataFrame, x_train: pd.DataFrame, y: np.ndarray, c
 class LDASettings(BaseClassifierSettings):
     """Holds the settings for the LDA object."""
 
-    def __init__(self, components: int | None = None, output: GraphMode = GraphMode.NONE, test_split: bool = False):
+    def __init__(self, components: int | None = None, output: str = 'none', test_split: bool = False):
         super().__init__(output, test_split)
         if components is not None and components <= 2:
             raise ValueError("Invalid component number: must be a > 1 integer.")

chemfusekit/lr.py

@@ -5,13 +5,13 @@
 import pandas as pd
 from sklearn.linear_model import LogisticRegression
 
-from chemfusekit.__utils import run_split_test, print_confusion_matrix, print_table, GraphMode
+from chemfusekit.__utils import run_split_test, print_confusion_matrix, print_table
 from .__base import BaseClassifierSettings, BaseDataModel, BaseClassifier, ReducerDataModel
 
 
 class LRSettings(BaseClassifierSettings):
     """Holds the settings for the LR object."""
-    def __init__(self, algorithm: str = 'liblinear', output: GraphMode = GraphMode.NONE, test_split: bool = False):
+    def __init__(self, algorithm: str = 'liblinear', output: str = 'none', test_split: bool = False):
         super().__init__(output, test_split)
         if algorithm not in [
             'lbfgs',

chemfusekit/pca.py

@@ -18,9 +18,8 @@
 class PCASettings(BaseSettings):
     """Holds the settings for the PCA object."""
 
-    def __init__(self, target_variance: float = 0.95,
-                 confidence_level: float = 0.05,
-                 initial_components: int = 10, output: GraphMode = GraphMode.NONE):
+    def __init__(self, target_variance: float = 0.95, confidence_level: float = 0.05, initial_components: int = 10,
+                 output: str = 'none'):
         super().__init__(output)
         if target_variance < 0:
             raise ValueError("Target variance should be positive or null.")

chemfusekit/plsda.py

@@ -8,14 +8,14 @@
 from sklearn.cross_decomposition import PLSRegression as PLSR
 from sklearn.model_selection import cross_val_score
 
-from chemfusekit.__utils import GraphMode, print_table, print_confusion_matrix, run_split_test
+from chemfusekit.__utils import print_table, print_confusion_matrix, run_split_test, GraphMode
 from .__base import BaseClassifierSettings, BaseDataModel, BaseClassifier, BaseReducer, ReducerDataModel
 
 
 class PLSDASettings(BaseClassifierSettings):
     """Holds the settings for the PLSDA object."""
 
-    def __init__(self, components: int | None = None, output: GraphMode = GraphMode.NONE, test_split: bool = False):
+    def __init__(self, components: int | None = None, output: str = 'none', test_split: bool = False):
         super().__init__(output, test_split)
         if components is not None and components < 1:
             raise ValueError("Invalid n_components number: should be a positive integer.")
@@ -136,7 +136,7 @@ def train(self):
         if self.settings.output and self.settings.test_split:
             x = self.data.x_data
             y = self.data.x_train.Substance.astype('category').cat.codes
-            run_split_test(x, y, PLSR(self.settings.components), mode=self.settings.output)
+            run_split_test(x, y, PLSR(self.components), mode=self.settings.output)
 
     def _select_feature_number(self, x, y):
         # Auto-select the number of components

chemfusekit/svm.py

@@ -3,13 +3,13 @@
 
 from sklearn.svm import SVC
 
-from chemfusekit.__utils import GraphMode, run_split_test, print_confusion_matrix
+from chemfusekit.__utils import run_split_test, print_confusion_matrix
 from .__base import BaseClassifierSettings, BaseClassifier, BaseDataModel
 
 
 class SVMSettings(BaseClassifierSettings):
     """Holds the settings for the SVM object."""
-    def __init__(self, kernel: str = 'linear', output: GraphMode = GraphMode.NONE, test_split: bool = False):
+    def __init__(self, kernel: str = 'linear', output: str = 'none', test_split: bool = False):
         super().__init__(output, test_split)
         if kernel not in ['linear', 'poly', 'gaussian', 'sigmoid']:
             raise ValueError("Invalid type: must be linear, poly, gaussian or sigmoid")

docs/docs/base/basesettings.md

@@ -10,11 +10,11 @@ Holds the settings for all classifier object. It's not meant for direct usage, o
 ## Syntax
 
 ```python
-BaseSettings(output: GraphMode, test_split: false)
+BaseSettings(output: str, test_split: false)
 ```
 
 ## Fields and constructor parameters
-- `output`: toggles graph output mode. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `test_split`: toggles the training split test phase. Defaults to `False`. Requires `output` to be set to `True` to work.
 
 The constructor raises:

docs/docs/base/reducerdatamodel.md

@@ -5,9 +5,9 @@ sidebar-position: 6
 
 # ReducerDataModel class
 
-This class models the output data for all dimensionality reduction operations (currently, the [`LDA`](../lda/lda), the [`PLSDA`](../plsda/plsda.md) and the [`PCA`](../pca/pca.md) operations).
+This class models the output data for all dimensionality reduction operations (currently, the [`LDA`](../lda/lda.md), the [`PLSDA`](../plsda/plsda.md) and the [`PCA`](../pca/pca.md) operations).
 
-It inherits from [`BaseDataModel`](../base/basedatamodel.md).
+It inherits from [`BaseDataModel`](./basedatamodel.md).
 
 ## Syntax
 

docs/docs/complete-workflow.md

@@ -5,25 +5,25 @@ sidebar-position: 7
 # Complete workflow
 
 Here's a sequence diagram to represent an example workflow, from the raw data
-tables to classification, including data fusion, PCA and training.
+tables to classification, including data fusion, dimensionality reduction and training.
 
 ```plantuml
 actor User
-participant LLDF
-participant PCA
+participant DF
+participant Reducer
 participant Classifier
 
-User -> LLDF : Upload training tables
-User -> LLDF : Set parameters
+User -> DF : Upload training tables
+User -> DF : Set parameters
 User -> Classifier : (optional) Upload model
 
-LLDF -> PCA  : Pass preprocessed / fused tables
-LLDF --> User  : Download fused tables
-LLDF -> Classifier  : Pass preprocessed / fused tables \nRun classification
-PCA -> Classifier : (optional) Set number of components
+DF -> Reducer  : Pass preprocessed / fused tables
+DF --> User  : Download fused tables
+DF -> Classifier  : Pass preprocessed / fused tables \nRun classification
+Reducer -> Classifier : (optional) Set number of components
 
 Classifier --> User : classification results, graphs
-PCA --> User : classification results, graphs
+Reducer --> User : classification results, graphs
 Classifier --> User : (optional) download trained model
 
 User -> Classifier : pass data to classify

docs/docs/df/dfsettings.md

@@ -2,18 +2,18 @@
 sidebar_position: 2
 ---
 
-# LLDFSettings class
+# DFSettings class
 
 Holds the settings for the [`DF`](./df-class.md) object.
 
 ## Syntax
 
 ```python
-DFSettings(output: GraphMode, method: str)
+DFSettings(output: str, method: str)
 ```
 
 ## Fields and constructor parameters
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `method`: a choice between `concat` (concatenation-based data fusion) and `outer` (outer matrix multiplication-based data fusion)
 
 The constructor raises a `ValueError("DF: invalid method: must be 'concat' or 'outer'")` if an invalid parameter is provided to the `method` field.
@@ -24,5 +24,5 @@ The constructor raises a `ValueError("DF: invalid method: must be 'concat' or 'o
 from chemfusekit.df import DFSettings
 
 # Initialize the settings for low-level data fusion
-df_settings = DFSettings(output=GraphMode.TEXT)
+df_settings = DFSettings(output='text')
 ```

docs/docs/knn/knn.md

@@ -20,9 +20,9 @@ KNN(settings: KNNSettings, data: LLDFModel)
 
 ## Fields
 
-- `settings`: object of type [`KNNSettings`](/tesi/docs/knn/knnsettings). Contains the settings for
+- `settings`: object of type [`KNNSettings`](./knnsettings.md). Contains the settings for
   the `KNN` object. 
-- `data`: onject of type ['DFModel`](/tesi/docs/df/dfmodel). Contains the
+- `data`: onject of type ['DFModel`](../df/dfmodel.md). Contains the
   artifacts from the data fusion process.
 - `model`: a `KNeighborsClassifier` model from `scikit-learn`. Defaults to `None`.
 

docs/docs/knn/knnsettings.md

@@ -16,7 +16,7 @@ KNNSettings(
   metric: str | Callable,
   weights: str | Callable,
   algorithm: str,
-  output: GraphMode,
+  output: str,
   test_split: false
 )
 ```
@@ -38,7 +38,7 @@ KNNSettings(
     - `kd_tree`
     - `brute`
   or be a callable object.
-- `output`: toggles graph output mode. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `test_split`: toggles the training split test phase. Defaults to `False`. Requires `output` to be set to `True` to work.
 
 The constructor raises:
@@ -51,14 +51,14 @@ The constructor raises:
 ## Example
 
 ```python
-from chemfusekit.knn import KNNSettings, GraphMode
+from chemfusekit.knn import KNNSettings
 
 settings = KNNSettings(
     n_neighbors=20,     # pick 20 neighbors
-    metric='minkowski', # choose the metric
-    weights='distance', # choose the weight metric
+    metric='minkowski',   # choose the metric
+    weights='distance',   # choose the weight metric
     algorithm='auto',   # the best algorithm gets chosen automatically
-    output=GraphMode.GRAPHIC, # graph output is enabled
-    test_split=True # the model will be split-tested at the end of the training
+    output='graphical',   # graph output is enabled
+    test_split=True   # the model will be split-tested at the end of the training
 )
 ```

docs/docs/lda/ldasettings.md

@@ -11,13 +11,13 @@ Inherits from [`BaseSettings`](../base/basesettings.md).
 ## Syntax
 
 ```python
-LDASettings(components: int, output: GraphMode, split_test: bool)
+LDASettings(components: int, output: str, split_test: bool)
 ```
 
 ## Fields and constructor parameters
 
 - `components`: the amount of components to be used in the LDA model. Defaults to 3.
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `test_split`: toggles split testing. Defaults to `False`.
 
 
@@ -29,11 +29,11 @@ The constructor raises:
 ## Example
 
 ```python
-from chemfusekit.lda import LDASettings, GraphMode
+from chemfusekit.lda import LDASettings
 
 settings = LDASettings(
     components=(pca.components - 1),    # one less component than the number determined by PCA
-    output=GraphMode.GRAPHIC, # graphs will be printed
-    test_split=True # split testing is enabled
+    output='graphical',   # graphs will be printed
+    test_split=True   # split testing is enabled
 )
 ```

docs/docs/lr/lrsettings.md

@@ -11,7 +11,7 @@ Inherits from [`BaseSettings`](../base/basesettings.md).
 ## Syntax
 
 ```python
-LRSettings(algorithm: str, output: GraphMode, test_split: bool)
+LRSettings(algorithm: str, output: str, test_split: bool)
 ```
 
 ## Fields and constructor parameters
@@ -23,7 +23,7 @@ LRSettings(algorithm: str, output: GraphMode, test_split: bool)
     - `newton-cholesky`
     - `sag`
     - `saga`
-- `output`: toggles graph output. Defaults to [`GraphMode.NONE`](../utils/graphmode.md).
+- `output`: toggles graph output. Defaults to [`none`] (other options: 'graphical', 'text'). Gets implicitly converted to a [`GraphMode` enum](../utils/graphmode.md).
 - `test_split`: toggles split testing. Defaults to `False`.
 
 The constructor raises:
@@ -34,11 +34,11 @@ The constructor raises:
 ## Example
 
 ```python
-from chemfusekit.lr import LRSettings, GraphMode
+from chemfusekit.lr import LRSettings
 
 settings = LRSettings(
     algorithm='newton-cg',
-    output=GraphMode.GRAPHIC,   # graphs will be printed
-    test_split=True # split testing is enabled
+    output='graphical',   # graphs will be printed
+    test_split=True   # split testing is enabled
 )
 ```

docs/docs/pca/pca.md

@@ -21,7 +21,7 @@ PCA(settings: PCASettings, data: BaseDataModel)
 
 ## Fields
 
-- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md.md). Contains the data to be analyzed.
+- `data`: object of type [`BaseDataModel`](../base/basedatamodel.md). Contains the data to be analyzed.
 - `components`: Number of components for the PCA analysis. Defaults to 0.
 - `model`: A `PCA` model from `scikit-learn`. Defaults to `None`.
 - `settings`: object of type [`PCASettings`](./pcasettings.md). Contains the settings for