Organize documentation

README.md

@@ -6,7 +6,7 @@
 To install `librep`, you may use:
 
 ```
-pip install git+https://github.com/otavioon/hiaac-librep.git
+pip install git+https://github.com/discovery-unicamp/hiaac-librep.git
 ```
 
 ### Pip optional dependencies
@@ -22,7 +22,7 @@ In order to contribute with `librep` you may want to:
 1. Clone librep's repository:
 
 ```
-git clone https://github.com/otavioon/hiaac-librep.git
+git clone https://github.com/discovery-unicamp/hiaac-librep.git
 ```
 
 2. Create a python virtual environment and activate it (requires Python >= 3.8):
@@ -36,7 +36,7 @@ source .librep-venv/bin/activate
 3. Install librep development packages, in editable mode
 
 ```
-pip install -e .[dev]
+pip install -e .[dev] (we need to fix the flag [dev])
 ```
 
 4. Run tests

src/librep/config/type_definitions.py

@@ -5,5 +5,6 @@
 
 # PathLike: The PathLike type is used for defining a file path.
 PathLike = Union[str, os.PathLike]
+# ArrayLike = Union[numpy.ndarray, list]
 ArrayLike = Union[numpy.ndarray, pd.DataFrame, Iterable]
 KeyType = Hashable

src/librep/estimators/sklearn.py

@@ -1,3 +1,5 @@
 from sklearn.neighbors import *
 from sklearn.ensemble import *
-from sklearn.svm import *
+from sklearn.svm import *
+from sklearn.tree import *
+from sklearn.linear_model import *

src/librep/metrics/report.py

@@ -4,10 +4,14 @@
     classification_report,
     accuracy_score,
     f1_score,
+    balanced_accuracy_score,
+    recall_score,
+    precision_score,
     confusion_matrix,
     ConfusionMatrixDisplay
 )
 import matplotlib.pyplot as plt
+import numpy as np
 
 from librep.base.evaluators import SupervisedEvaluator
 from librep.config.type_definitions import ArrayLike, PathLike
@@ -18,6 +22,10 @@ def __init__(
         self,
         use_accuracy: bool = True,
         use_f1_score: bool = True,
+        use_balanced_accuracy: bool = False,
+        use_precision: bool = True,
+        use_recall: bool = True,
+        use_geometric_mean: bool = True,
         use_confusion_matrix: bool = True,
         use_classification_report: bool = False,
         plot_confusion_matrix: bool = True,
@@ -29,6 +37,10 @@ def __init__(
     ):
         self.use_accuracy = use_accuracy
         self.use_f1_score = use_f1_score
+        self.use_balanced_accuracy = use_balanced_accuracy
+        self.use_precision = use_precision
+        self.use_recall = use_recall
+        self.use_geometric_mean = use_geometric_mean
         self.use_confusion_matrix = use_confusion_matrix
         self.use_classification_report = use_classification_report
         self.plot_confusion_matrix = plot_confusion_matrix
@@ -59,6 +71,32 @@ def evaluate(
 
             res = f1_score(y_true, y_pred, average="macro")
             result["f1 score (macro)"] = float(res)
+
+        if self.use_balanced_accuracy:
+            res = balanced_accuracy_score(y_true, y_pred)
+            result["balanced accuracy"] = float(res)
+        if self.use_precision:
+            res = precision_score(y_true, y_pred, average="weighted")
+            result["precision (weighted)"] = float(res)
+
+            res = precision_score(y_true, y_pred, average="micro")
+            result["precision (micro)"] = float(res)
+
+            res = precision_score(y_true, y_pred, average="macro")
+            result["precision (macro)"] = float(res)
+        if self.use_recall:
+            res = recall_score(y_true, y_pred, average="weighted")
+            result["recall (weighted)"] = float(res)
+
+            res = recall_score(y_true, y_pred, average="micro")
+            result["recall (micro)"] = float(res)
+
+            res = recall_score(y_true, y_pred, average="macro")
+            result["recall (macro)"] = float(res)
+
+        if self.use_geometric_mean:
+            res = np.sqrt(recall_score(y_true, y_pred, average="weighted") * precision_score(y_true, y_pred, average="weighted"))
+            result["geometric mean"] = float(res)
 
         if self.use_confusion_matrix:
             res = confusion_matrix(y_true, y_pred)

src/librep/transforms/__init__.py

@@ -1,9 +1,22 @@
+from .autocorrelation import *
+from .convaelstm import *
+from .dimal import *
 from .fft import *
+from .filter import *
+from .ica import *
+from .isomap import *
+from .kernel_pca import *
+from .lle import *
 from .lstm import *
+from .pca import *
+from .removeFrequencies import *
 from .resampler import *
-from .umap import *
-from .tsne import *
-from .autocorrelation import *
+from .reshaper import *
+from .simclr_full import *
+from .simclr_linear import *
+from .simclr import *
+from .spectrogram import *
 from .stats import *
-from .removeFrequencies import *
+from .topo_ae import *
 from .tsne import *
+from .umap import *

src/librep/transforms/fft.py

@@ -61,6 +61,8 @@ def transform(self, X: ArrayLike) -> ArrayLike:
             data = fftpack.fft(data)
             if self.absolute:
                 data = np.abs(data)
+            else:
+                data = np.real(data)
             if self.centered:
                 data = data[:len(data)//2]
             datas.append(data)

src/librep/transforms/ica.py

@@ -0,0 +1,3 @@
+from sklearn.decomposition import FastICA as reducer
+
+FastICA = reducer

src/librep/transforms/isomap.py

@@ -0,0 +1,3 @@
+from sklearn.manifold import Isomap as reducer
+
+Isomap = reducer

src/librep/transforms/kernel_pca.py

@@ -0,0 +1,3 @@
+from sklearn.decomposition import KernelPCA as reducer
+
+KernelPCA = reducer

src/librep/transforms/lle.py

@@ -0,0 +1,3 @@
+from sklearn.manifold import LocallyLinearEmbedding as reducer
+
+LocallyLinearEmbedding = reducer