pyproject.toml

@@ -21,8 +21,7 @@ dependencies = [
     "matplotlib>=3.6",
     "numpy",
     "torch",
-    "scikit-learn",
+    "scikit-learn"
-    "pandas",
 ]
 
 [project.urls]

readme.md

@@ -1,17 +1,13 @@
-# Machine learning for material recognition with a triboelectric nanogenerator (TENG)
+# Machine learning for material recognition with a TENG
-This project was written for my bachelor's thesis.
+(Bi)LSTM for name classification.  
-
+More information on the project are [on my website](https://quintern.xyz/en/teng.html).
-It was written to classify TENG voltage output from pressing it against different materials.
-Contents:
-- Data preparation/plotting/loading utilites
-- (Bi)LSTM + fully connected + softmax model for name classifiying TENG output
-- Progress tracking utilities to easily find the best parameters
 
 ## Model training
-Adjust the parameters in `main.py` and run it.
+Adjust the parameters in `main.py` and run it.  
 All models and the settings they were trained with are automatically serialized with pickle and stored in a subfolder
 of the `<model_dir>` that was set in `main.py`.
 
 
 ## Model evaluation
 Run `find_best_model.py <model_dir>` with the `<model_dir>` specified in `main.py` during training.
+

teng_ml/main.py

@@ -42,10 +42,9 @@ def test_interpol():
 
 
 if __name__ == "__main__":
-    # labels = LabelConverter(["foam_PDMS_white", "foam_PDMS_black", "foam_PDMS_TX100", "foam_PE", "antistatic_foil", "cardboard", "glass", "kapton", "bubble_wrap_PE", "fabric_PP" ])
+    labels = LabelConverter(["foam_PDMS_white", "foam_PDMS_black", "foam_PDMS_TX100", "foam_PE", "antistatic_foil", "cardboard", "glass", "kapton", "bubble_wrap_PE", "fabric_PP", ])
-    labels = LabelConverter(["foam_PDMS_white", "foam_PDMS_black", "foam_PDMS_TX100", "foam_PE", "antistatic_foil", "cardboard", "kapton", "bubble_wrap_PE", "fabric_PP" ])
+    # labels = LabelConverter(["foam_PDMS_white", "foam_PDMS_black", "foam_PDMS_TX100", "foam_PE", "kapton", "bubble_wrap_PE", "fabric_PP", ])
-    # labels = LabelConverter(["foam_PDMS_white", "foam_PDMS_black", "foam_PDMS_TX100", "foam_PE", "kapton", "bubble_wrap_PE", "fabric_PP" ])
+    models_dir = "/home/matth/Uni/TENG/teng_2/models_gen_12"  # where to save models, settings and results
-    models_dir = "/home/matth/Uni/TENG/teng_2/models_gen_15"  # where to save models, settings and results
     if not path.isdir(models_dir):
         makedirs(models_dir)
     data_dir = "/home/matth/Uni/TENG/teng_2/sorted_data"
@@ -54,18 +53,18 @@ if __name__ == "__main__":
     # gen_6 best options: no glass, cardboard and antistatic_foil, not bidirectional, lr=0.0007, no datasplitter, 2 layers n_hidden = 10
 
     # Test with
-    num_layers = [ 4, 5 ]
+    num_layers = [ 2, 3 ]
-    hidden_size = [ 28, 36 ]
+    hidden_size = [ 21, 28 ]
-    bidirectional = [ True ]
+    bidirectional = [ False, True ]
     t_const_int = ConstantInterval(0.01)  # TODO check if needed: data was taken at equal rate, but it isnt perfect -> maybe just ignore?
     t_norm = Normalize(-1, 1)
-    transforms = [[]] #, [ t_norm, t_const_int ]]
+    transforms = [[ t_norm ]]  #, [ t_norm, t_const_int ]]
     batch_sizes = [ 4 ]
     splitters = [ DataSplitter(50, drop_if_smaller_than=30) ]  # smallest file has length 68 TODO: try with 0.5-1second snippets
     num_epochs = [ 80 ]
     # (epoch, min_accuracy)
-    # training_cancel_points = [(15, 20),  (40, 25)]
+    training_cancel_points = [(15, 20),  (40, 25)]
-    training_cancel_points = []
+    # training_cancel_points = []
 
     args = [num_layers, hidden_size, bidirectional, [None], [None], [None], transforms, splitters, num_epochs, batch_sizes]
 
@@ -82,7 +81,7 @@ if __name__ == "__main__":
         None,
         # lambda optimizer, st: torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9),
         # lambda optimizer, st: torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.5),
-        # lambda optimizer, st: torch.optim.lr_scheduler.StepLR(optimizer, step_size=st.num_epochs // 8, gamma=0.60, verbose=False),
+        lambda optimizer, st: torch.optim.lr_scheduler.StepLR(optimizer, step_size=st.num_epochs // 8, gamma=0.60, verbose=False),
         # lambda optimizer, st: torch.optim.lr_scheduler.StepLR(optimizer, step_size=st.num_epochs // 10, gamma=0.75, verbose=False),
     ]
 

teng_ml/tracker/epoch_tracker.py

@@ -111,28 +111,26 @@ class EpochTracker:
         """
         @param model_dir: Optional. If given, save to model_dir as svg
         """
-        fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, layout="tight", figsize=(6, 6))
+        fig, ax = plt.subplots(nrows=3, ncols=1, sharex=True, layout="tight")
 
         ax[0].plot(self.epochs, self.accuracies, color="red")
         ax[0].set_ylabel("Accuracy")
-        ax[0].grid("minor")
 
         ax[1].plot(self.epochs, self.learning_rate, color="green")
         ax[1].set_ylabel("Learning Rate")
-        ax[1].grid("minor")
 
-        # ax[2].plot(self.epochs, self.loss, color="blue")
+        ax[2].plot(self.epochs, self.loss, color="blue")
-        # ax[2].set_ylabel("Loss")
+        ax[2].set_ylabel("Loss")
 
         fig.suptitle(title)
-        ax[-1].set_xlabel("Epoch")
+        ax[2].set_xlabel("Epoch")
         plt.tight_layout()
         if model_dir is not None:
             fig.savefig(f"{model_dir}/{name}.svg")
 
         return fig, ax
 
-    def plot_predictions(self, title="Predictions per Label", ep=-1, model_dir=None, name="img_training_predictions", empty_zero=True):
+    def plot_predictions(self, title="Predictions per Label", ep=-1, model_dir=None, name="img_training_predictions"):
         """
         @param model_dir: Optional. If given, save to model_dir as svg
         @param ep: Epoch, defaults to last
@@ -143,23 +141,8 @@ class EpochTracker:
 
         N = len(self.labels)
         label_names = self.labels.get_labels()
-        # print(label_names)
-        replace = {
-            "cloth": "fabric",
-            "foam": "foam_PDMS_pure",
-            "foil": "bubble_wrap",
-            "rigid_foam": "foam_PE",
-            "fabric_PP": "fabric",
-            "foam_PDMS_white": "foam_PDMS_pure",
-            "foam_PDMS_black": "foam_PEDOT",
-            "bubble_wrap_PE": "bubble_wrap",
-        }
-        label_names = [ replace[label] if label in replace else label for label in label_names ]
 
-        if len(label_names) > 6:
+        fig, ax = plt.subplots(layout="tight")
-            fig, ax = plt.subplots(layout="tight", figsize=(7, 6))
-        else:
-            fig, ax = plt.subplots(layout="tight", figsize=(6, 5))
         im = ax.imshow(normalized_predictions, cmap='Blues')  # cmap='BuPu', , norm=colors.PowerNorm(1./2.)
         ax.set_xticks(np.arange(N))
         ax.set_yticks(np.arange(N))
@@ -172,21 +155,14 @@ class EpochTracker:
         for i in range(1, N):
             ax.axhline(i-0.5, color='black', linewidth=1)
 
-        # for i in range(1, N):
-            # ax.axvline(i-0.5, color='#bbb', linewidth=1)
-
         # rotate the x-axis labels for better readability
         plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
 
         # create annotations
         for i in range(N):
             for j in range(N):
-                val = round(normalized_predictions[i, j], 2)
+                text = ax.text(j, i, round(normalized_predictions[i, j], 2),
-                if empty_zero and val == 0: continue
+                               ha="center", va="center", color="black")
-                color = "black"
-                if normalized_predictions[i, j] >= 0.6: color = "white"
-                text = ax.text(j, i, val,
-                               ha="center", va="center", color=color)
 
         # add colorbar
         cbar = ax.figure.colorbar(im, ax=ax)

teng_ml/util/data_loader.py

@@ -10,13 +10,8 @@ import threading
 
 from sklearn.model_selection import train_test_split
 
-from teng_ml.util.transform import Multiply
-
 # groups: date, name, n_object, voltage, distance, index
 # re_filename = r"(\d{4}-\d{2}-\d{2})_([a-zA-Z_]+)_(\d{1,2}(?:\.\d*)?)V_(\d+(?:\.\d*)?)mm(\d+).csv"
-# for teng_1
-# re_filename = r"(\d{4}-\d{2}-\d{2})_([a-zA-Z_]+)_()(\d{1,2}(?:\.\d*)?)V_(\d+(?:\.\d*)?)mm(\d+).csv"
-# for teng_2
 re_filename = r"(\d{4}-\d{2}-\d{2})_([a-zA-Z0-9_]+)_(\d+)_(\d{1,2}(?:\.\d*)?)V_(\d+(?:\.\d*)?)mm(\d+).csv"
 
 class LabelConverter:
@@ -56,7 +51,7 @@ class Datasample:
     def __init__(self, date: str, label: str, n_object: str, voltage: str, distance: str, index: str, label_vec, datapath: str, init_data=False):
         self.date = date
         self.label = label
-        self.n_object = 0 if n_object == "" else int(n_object)
+        self.n_object = int(n_object)
         self.voltage = float(voltage)
         self.distance = float(distance)
         self.index = int(index)
@@ -91,19 +86,6 @@ class Dataset:
         """
         self.transforms = transforms
         self.data = []  # (data, label)
-
-        # NORMALIZE ALL DATA WITH THE SAME FACTOR
-        # sup = 0
-        # inf = 0
-        # for sample in datasamples:
-        #     data = sample.get_data()
-        #     max_ = np.max(data[:,2])
-        #     min_ = np.min(data[:,2])
-        #     if max_ > sup: sup = max_
-        #     if min_ < inf: inf = min_
-        # multiplier = 1 / max(sup, abs(inf))
-        # self.transforms.append(Multiply(multiplier))
-
         for sample in datasamples:
             data = self.apply_transforms(sample.get_data())
             if split_function is None:
@@ -146,7 +128,7 @@ def get_datafiles(datadir, labels: LabelConverter, exclude_n_object=None, filter
         label = match.groups()[1]
         if label not in labels: continue
 
-        sample_n_object = 0 if match.groups()[2] == "" else int(match.groups()[2])
+        sample_n_object = float(match.groups()[2])
         if exclude_n_object and exclude_n_object == sample_n_object: continue
         sample_voltage = float(match.groups()[3])
         if filter_voltage and filter_voltage != sample_voltage: continue

teng_ml/util/transform.py

@@ -1,6 +1,5 @@
 import numpy as np
 from scipy.interpolate import interp1d
-from torch import mul
 
 class Normalize:
     """
@@ -42,15 +41,6 @@ class NormalizeAmplitude:
         return f"NormalizeAmplitude(high={self.high})"
 
 
-class Multiply:
-    def __init__(self, multiplier):
-        self.multiplier = multiplier
-    def __call__(self, data):
-        return data * self.multiplier
-    def __repr__(self):
-        return f"Multiply(multiplier={self.multiplier})"
-
-
 class ConstantInterval:
     """
     Interpolate the data to have a constant interval / sample rate,