Machine learning software for rapid Spectral analysis. While Raman spectra were the initilal focus, SpectralMachine is flexible to be applied for classification using any spectra (from XRD, FTIR and beyond). The current version 2 is the stable and recommended for use. Version 1 is no longer supported.
Supported algorithms:
- Deep Neural Networks:
- multi-layer perceptron (MLP) (L-BFGS Optimizer strongly recommended)
- DNNClassifier (TensorFlow and keras)
- Convolutional Neural Networks (Under development - via keras)
- Support Vector Machine - SVM
- TensorFlow (basic implementation)
Additional multivariate analysis:
- K-Means
- Principal component analysis
If you use SpectralMachine, we request that you reference the papers/resources on which SpectralMachine is based:
- N Ferralis, SpectralMachine (2017), https://github.com/feranick/SpectralMachine
- N. Ferralis, JC Grossman, RE Summons, "Mineral and Geochemical Classification from Spectroscopy/Diffraction Through Neural Networks", AGU Fall Meeting 2017, New Orleans, LA.
This software requires Python (3.3 or higher). It has been tested with Python 3.5 or higher which is the recommended platform. It is not compatible with python 2.x. Additional required packages:
numpy
scikit-learn (>=0.18)
matplotlib
pandas
keras
pydot
graphviz
h5py
In addition, these packages may be needed depending on your platform (via apt-get in debian/ubuntu or port in OSX):
python3-tk
graphviz
These are found in Unix based systems using common repositories (apt-get for Debian/Ubuntu Linux, or MacPorts for MacOS). More details in the scikit-learn installation page.
TensorFlow is needed only if flag is activated. Instructions for Linux and MacOS can be found in TensorFlow installation page. Pip installation is the easiest way to get going. Tested with TensorFlow v.1.4+ (not compatible with v.1.3 and below).
Single files:
python3 SpectraLearnPredict.py -f learningfile spectrafile
Cross-validation for accuracy determination:
python3 SpectraLearnPredict.py -a learningfile testdataset
Cross-validation for accuracy determination (automatic splitting):
python3 SpectraLearnPredict.py -a <learningfile>
Maps (formatted for Horiba LabSpec):
python3 SpectraLearnPredict.py -m learningfile spectramap
Batch txt files:
python3 SpectraLearnPredict.py -b learningfile
K-means on Raman maps:
python3 SpectraLearnPredict.py -k spectramap number_of_classes
Principal component analysis on spectral collection files:
python3 SpectraLearnPredict.py -p spectrafile #comp
Run in background for accuracy determination during training:
python3 SpectraLearnPredict.py -a learningfile testdataset 2>&1 | tee -a logfile &
We do not provide advanced training sets, some of which can be found online. We only provide a simple Raman dataset mainly for testing purposes: it is loosely based on 633nm data from Ferralis et al. Carbon 108 (2016) 440.
Using the provided training data set as is, accuracy is low. For a single training run using a random 30% of the training set for 100 times, the accuracy is about 32%:
./SpectraLearnPredict.py -t Training/20170227a/Training_kerogen_633nm_HC_20170227a.txt 1
Repeating the training few more times (5, for example) marginally increases the accuracy to 35.7% and it is fully converged. This is expected given the small dataset.
Increasing the number of spectra in the actual dataset can be done by accounting for noise. Using the AddNoisyData.py utility, the esisting training set is taken, and random variations in intensity at each energy point are added within a given offset. This virtually changes the overall spectra, without changing its overall trend in relation to the classifier (H:C). This allows for the preservation of the classifier for a given spectra, but it also increases the number of available spectra. This method is obviously a workaround, but it allows accuracy to be substantially increased. Furthermore, it lends a model better suited at dealing with noisy data.
To recreate, let's start with adding noisy spectra to the training set. For example, let's add 5 replicated spectra with random noise added with an offset of 0.02 (intensity is normalized to the range [0,1])
AddNoisyData.py Training/20170227a/Training_kerogen_633nm_HC_20170227a.txt 5 0.02
Accuracy is increased to 80.4% with a single training run (100 times 30% of the dataset). 2 iterations increase accuracy to 95.8% and a third increased to 100%. (The same can be achieved by running 30% of the dataset 300 times).
One can optimize/minimize the number of spectra with added noise. Adding only 2 data-sets with noise offset at 0.02 converges the accuracy to about 94.6%.
One final word of caution: Increasing the number of statistically independent available spectra for training is recommended over adding noisy data.
- lbfgs is an optimizer in the family of quasi-Newton methods
- sgd, refers to stochastic gradient descent.
- adam refers to a stochastic gradient-based optimizer
- Adagrad (default)
- Adam
- Ftrl
- Momentum
- RMSProp
- SGD
- identity, no-op activation, useful to implement linear bottleneck, returns f(x) = x
- logistic, the logistic sigmoid function, returns f(x) = 1 / (1 + exp(-x)).
- tanh, the hyperbolic tan function, returns f(x) = tanh(x).
- relu, the rectified linear unit function, returns f(x) = max(0, x)
- leaky_relu: tf.nn.relu(x) - alpha * tf.nn.relu(-x) - Alpha = 0.2
- relu, rectified linear: max(features, 0)
- relu6, rectified linear 6: min(max(features, 0), 6)
- crelu, concatenated ReLU. Concatenates a ReLU which selects only the positive part of the activation with a ReLU which selects only the negative part of the activation.
- elu, exponential linear: exp(features) - 1 if < 0, features otherwise.
- softplus, log(exp(features) + 1)
- softsign, features / (abs(features) + 1)
- dropout
- bias_add, bias to value
- sigmoid, sigmoid of x element-wise, y = 1 / (1 + exp(-x))
- tanh, hyperbolic tangent of x element-wise