swectral 0.6.3

A Python framework for automated batch composition, implementation and method assessment of plant hyperspectral modeling pipelines.

Swectral

A Python framework for automated batch composition, implementation and method assessment of plant hyperspectral modeling pipelines.

Swectral streamlines the batch testing and optimization of plant hyperspectral analysis workflows. It provides a structured and extensible framework to apply and assess various image processing techniques (calibration, baseline correction, denoising, feature engineering, etc.) in combination with various machine learning models. The framework employs a comprehensive full-factorial design to evaluate all method combinations on user spectral dataset and generates standard reports on performance metrics, comparative statistical tests, residual analysis, influence anlaysis and visualizations.

Core features

• Batch processing: Automate numerous data processing and modeling workflows in a single batch operation.
• File-based: A resumable, file-based processing pipeline with full-scale auditability and break tolerance.
• High-performance: Optimized for hyperspectral images with minimal memory consumption and options of GPU acceleration and pipeline-level multiprocessing.
• Simple extensible integration: Intuitive data management and straightforward integration for custom processing functions and Scikit-learn-style models.

Table of Contents

• Installation
• Usage
• Contributing
• License

Documentation

• User Guide
• API Reference
• Examples

Installation

Follow these steps to install the project:

1. Prerequisites: Ensure you have Python 3.9 or higher installed.

2. Install from PyPI (Recommended):

   pip install swectral
   

3. Install from source (for development):

   git clone https://github.com/siwei66/swectral.git
   cd swectral
   pip install -e swectral
   

Usage

1. Data preparation

• Setup a demo directory in current working directory

  import os
  demo_dir = os.getcwd() + "/SpecPipeDemo/"
  

• Create a data directory and download real-world demo data

  data_dir = demo_dir + "demo_data/"
  os.makedirs(data_dir)
  
  from swectral import download_demo_data
  download_demo_data(data_dir)
  

• Create a directory for pipeline results

  report_dir = demo_dir + "/demo_results_classification/"
  os.makedirs(report_dir)
  

2. Data configuration

• Create a SpecExp instance:

  from swectral import SpecExp
  exp = SpecExp(report_dir)
  

The instance stores and organizes the data loading configurations of an experiment, which faciliates lazy-loading.

• Check report directory:

  exp.report_directory
  

  Output:

  '~/SpecPipeDemo/demo_results_classification/'
  

• Add experiment groups:

  exp.add_groups(['group_1', 'group_2'])
  

• Add raster images:

  exp.add_images_by_name(image_name="demo.", image_directory=data_dir, group="group_1")
  exp.add_images_by_name("demo.", data_dir, "group_2")
  

  Output:

  Following image items are added:
      Group    Image    Mask
  0   group_1  demo.tiff
  

• Load image ROIs using suffix to image names:

  # By parameter name
  exp.add_rois_by_suffix(roi_filename_suffix="_[12].xml", search_directory=data_dir, group="group_1")
  # Or by parameter position
  exp.add_rois_by_suffix("_[345].xml", data_dir, "group_2")
  

  Output:

  Following ROI items loaded:
     Group    Image    ROI_name    ROI_type    ROI_source_file
  0  group_1  demo.tiff      1-1   sample      demo_1.xml
  1  group_1  demo.tiff      1-2   sample      demo_1.xml
  ...
  9  group_1  demo.tiff      2-5   sample      demo_2.xml
  

• Show raster RGB preview with associated ROIs:

  exp.show_image("demo.tiff", "group_1", rgb_band_index=(19, 12, 6), output_path=report_dir + "demo_rast_rgb1.png")
  

  Output:

  

  exp.show_image("demo.tiff", "group_2", rgb_band_index=(19, 12, 6), output_path=report_dir + "demo_rast_rgb2.png")
  

  Output:

  

2.5. Sample labels and target values

2.5.1 Set sample labels

• Get current sample label dataframe:

  labels = exp.ls_labels()
  

• Set new sample labels in the dataframe:

  Here we use sample ROI names as sample labels:

  labels.iloc[:, 1] = exp.ls_rois_sample(return_dataframe=True, print_result=False)["ROI_name"]
  

• Update sample labels:

  exp.sample_labels = labels
  

• Check sample labels:

  exp.ls_labels()["Label"]
  

  Output:

  0     1-1
  1     1-2
  ...
  24    5-5
  

2.5.2 Set target values

• List target value dataframe:

  targets = exp.ls_sample_targets()
  

• Create mock target values for regression and update target dataframe:

  Here we use leaf number:

  targets["Target_value"] = [f"leaf_{labl[0]}" for labl in targets['Label']]
  

• Load target values from updated target dataframe:

  exp.sample_targets_from_df(targets)
  

• Check target values:

  exp.ls_targets()[["Label", "Target_value"]]
  

  Output:

      Label Target_value
  0    1-1       leaf_1
  1    1-2       leaf_1
  ...
  24   5-5       leaf_5
  

3. Design testing pipelines

• SpecPipe follows a structured data processing workflow with these sequential data levels:

  Raster image data -> ROI spectra -> ROI statistics -> Traits to model
  

• The data levels in SpecPipe includes:

  Raster images:
      0 - "image", input image path and output processed image path.
  
      1 - "pixel_spec", if the process callable is applied to 1D spectrum of image pixel
  
      2 - "pixel_specs_array", if the process callable is applied to 2D spectra array of image pixels
  
      3 - "pixel_specs_tensor", if the process callable is applied to 3D spectra tensor of image pixels
  
      4 - "pixel_hyperspecs_tensor", same as "pixel_specs_tensor" but optimized for hyperspectral images
  
  ROI spectra:
      5 - "image_roi", raster with sample ROIs, for spectrum extraction
  
      6 - "roispecs", 2D array of ROI spectra
  
  ROI statistics:
      7 - "spec1d", arbitrary 1D data of samples, e.g. 1D spectra, flattened spectra statistical metrics
  
  Sample data:
      8 - "assembly", sample data list for cross-sample interaction
  
  Models:
      9 - "model", model evaluation with standard report output as files
  

• The corresponding data processing workflow is:

  Raster image processing:           0 ~ 4
      ↓
  Extract ROI spectra:               5 - "image_roi"
      ↓
  ROI spectra manipulation:          6 - "roispecs"
      ↓
  Summarized ROI spectra:            7 - "spec1d"
      ↓
  Sample assembly:                   8 - "assembly"
      ↓
  Modeling and model evaluation:     9 - "model"
  

The processing functions are incorporated in the pipeline according to the specified "data levels". Parallel processes can be added with identical "data level" and "application sequence", and they are arranged using full-factorial approach in the pipeline.

3.1 Create processing pipeline

• Create processing pipeline from SpecExp instance configured above:

  from swectral import SpecPipe
  pipe = SpecPipe(exp)
  

3.2 Image processing

• Create some image processing functions, such as:

• Standard normal variate:

  from swectral.functions import snv
  

• Pass-through method for comparison:

  def raw(v): return v
  

3.3 ROI statistics

• Import spectral statistic metrics for ROI summary:

  from swectral import roi_mean, roi_median
  

3.4 Add models to the pipeline

• Create some models:

  from sklearn.ensemble import RandomForestClassifier
  from sklearn.neighbors import KNeighborsClassifier
  
  rf_classifier = RandomForestRegressor(n_estimators=10)
  knn_classifier = KNeighborsRegressor(n_neighbors=3)
  

3.5 Compose and check pipelines

• Compose pipelines:

  pipe.build_pipeline(
      [
          # 1 Image-wide baseline correction
          ((2, 2), [raw, snv]),
          # 2 ROI statistics
          ((5, 7), [roi_mean, roi_median]),
          # 3 Models (Feature selector included)
          ((7, 9), [rf_classifier, knn_classifier], {'validation_method': '2-fold'})
      ]
  )
  

• Check all processes including models:

  pipe.ls_process()
  

  Output:

       Step_0    Step_1         Step_2
  0    snv       roi_mean       KNeighborsClassifier
  1    snv       roi_mean       RandomForestClassifier
  2    snv       roi_median     KNeighborsClassifier
  3    snv       roi_median     RandomForestClassifier
  4    raw       roi_mean       KNeighborsClassifier
  5    raw       roi_mean       RandomForestClassifier
  6    raw       roi_median     KNeighborsClassifier
  7    raw       roi_median     RandomForestClassifier
  

4 Execute pipelines

• Run:

  pipe.run()
  

5 Generated reports

• Pipeline execution data is saved to local storage, use the methods to retrieve reports in the console:

  result_summary = pipe.report_summary()
  chain_results = pipe.report_chains()
  

• Check summary reports

  The summary reports include:

  result_summary.keys()
  

  Output:

  dict_keys([
      'Macro_avg_performance_summary',
      'Marginal_macro_avg_AUC_stats_step_0',
      'Marginal_macro_avg_AUC_stats_step_1',
      'Marginal_macro_avg_AUC_stats_step_2',
      'Marginal_micro_avg_AUC_stats_step_0',
      'Marginal_micro_avg_AUC_stats_step_1',
      'Marginal_micro_avg_AUC_stats_step_2',
      'Micro_avg_performance_summary',
      'sample_targets_stats'])
  

  Demonstration of macro-average performance metrics of classification:

  result_summary['Macro_avg_performance_summary']
  

  Output:

      Step_0   Step_1   Step_2  Precision  Recall  F1_Score  Accuracy    AUC
  0  2_0_%#1  5_0_%#1  7_0_%#1   0.860000    0.84  0.842828     0.936  0.947
  ...
  7  2_0_%#2  5_0_%#2  7_0_%#2   0.769524    0.72  0.684242     0.888  0.829
  

  Demonstration of marginal macro-average performance metrics of classification:

  result_summary['Marginal_macro_avg_AUC_stats_step_0']
  

  Output:

           Process_ID       All   2_0_%#1   2_0_%#2
  0     Process_label       All       snv       raw
  1         n_records         8         4         4
  2    Mean_AUC_macro   0.85425   0.95275   0.75575
  3     Min_AUC_macro     0.631     0.942     0.631
  4  Median_AUC_macro     0.906    0.9495     0.761
  5     Max_AUC_macro      0.97      0.97      0.87
  6               All       1.0  0.199557  0.199557
  7           2_0_%#1  0.199557       1.0  0.028571
  8           2_0_%#2  0.199557  0.028571       1.0
  

  The processes of the step (here raw image and standard normal variates) are compared using non-parametric Mann-Whitney-U test.

  Demonstration of Receiver-Operating-Characteristic curve:

  chain_results[0]['ROC_curve']
  

  Output:

  

6 Regression demonstration

6.1 Create a directory for regression results

• Create a directory for regression results

  report_dir_reg = demo_dir + "/demo_results_regression/"
  os.makedirs(report_dir_reg)
  

6.2 Copy and update the previous pipelines to regression

• Copy and update SpecExp and SpecPipe instances

  import copy
  
  exp_reg = copy.deepcopy(exp)
  pipe_reg = copy.deepcopy(pipe)
  targets_reg = copy.deepcopy(targets)
  

• Update report directory of SpecExp

  exp_reg.report_directory = report_dir_reg
  

• Modify targets to numeric, here the numbers approaximate the age of the leaves

  targets_reg["Target_value"] = [(5 - int(labl[0])) for labl in targets['Label']]
  

• Specify the ROIs within a same leaf to a validation group to prevent data leakage

  targets_reg["Validation_group"] = [f"leaf_{labl[0]}" for labl in targets['Label']]
  

• Update target information using the modified target dataframe

  exp_reg.sample_targets_from_df(targets_reg)
  

• Check target values and validation groups

  exp_reg.ls_targets()[["Label", "Target_value", "Validation_group"]]
  

6.3 Update the pipeline models to regressors

• Check and remove classification models

  pipe_reg.ls_model()
  pipe_reg.rm_model()
  

• Update the data manager

  pipe_reg.spec_exp = exp_reg
  

• Add regressors to the pipeline

  Add some regressors:

  from sklearn.ensemble import RandomForestRegressor
  from sklearn.neighbors import KNeighborsRegressor
  
  rf_regressor = RandomForestRegressor(n_estimators=10)
  knn_regressor = KNeighborsRegressor(n_neighbors=3)
  
  pipe_reg.add_model([knn_regressor, rf_regressor], validation_method="2-fold")
  

6.4 Execute regression pipelines

• Run:

  pipe_reg.run()
  

6.5 Check results of regression pipelines

• Retrieve reports in console

  result_summary_reg = pipe_reg.report_summary()
  chain_results_reg = pipe_reg.report_chains()
  

• Check summary reports

  The summary reports include:

  result_summary_reg.keys()
  

  Output:

  dict_keys([
      'Marginal_R2_stats_step_0',
      'Marginal_R2_stats_step_1',
      'Marginal_R2_stats_step_2',
      'Performance_summary',
      'sample_targets_stats'])
  

  Demonstration of performance summary content:

  result_summary_reg['Performance_summary'].columns
  

  Output:

  Index([
      'Step_0', 'Step_1', 'Step_2',
      'Mean_Error', 'Standard_Deviation_of_Error', 'Mean_Absolute_Error',
      'Normalized_MAE', 'CV_MAE',
      'Mean_Squared_Error', 'Root_Mean_Squared_Error',
      'Normalized_RMSE', 'CV_RMSE',
      'Residual_Prediction_Deviation', 'R2'
  ], dtype='object')
  

• Check processing chain reports

  For each chain, the reports include:

  chain_results_reg[0].keys()
  

  Output:

  dict_keys([
      'Chain_processes',
      'Regression_performance',
      'Residual_analysis',
      'Residual_plot',
      'Scatter_plot',
      'Validation_results'])
  

  Demonstration of the scatter plot of the processing chain:

  chain_results_reg[0]['Scatter_plot']
  

  Output:

  

7 Feature engineering fittable tests

Feature engineering and resampling fittables (data transformers and resamplers) are fitted during the model validation process and function as integrated parts of the model. To incorporate these transformers, use the model connector functions combine_classifier or combine_regressor (similar to sklearn.pipeline.Pipeline but more flexible and enable swectral pipeline analysis).

• This module includes a composer that generates batchwise combined models using a full factorial design:

  from sklearn.preprocessing import StandardScaler
  from sklearn.feature_selection import SelectKBest, f_classif
  from swectral import IdentityTransformer  # Passthrough transformer for comparison
  
  selector1 = SelectKBest(f_classif, k=5)  # Select 5 features
  selector2 = IdentityTransformer()  # For passthrough (no selection)
  
  from swectral import factorial_model_chains
  
  models = factorial_model_chains(
      [StandardScaler(), IdentityTransformer()],  # Model step 1: test data scalers
      {'Feat5': selector1, 'FeatAll': selector2},  # Model step 2: test feature selection fittables
      # ...
      estimators={'KNN': knn_classifier, 'RF': rf_classifier},  # Estimators (specify custom labels using dictionary input)
      is_regression=False
  )
  print(models)
  

  Output:

  [CombinedClassifier_StandardScaler_Feat5_KNN,
   CombinedClassifier_StandardScaler_Feat5_RF,
   CombinedClassifier_StandardScaler_FeatAll_KNN,
   CombinedClassifier_StandardScaler_FeatAll_RF,
   CombinedClassifier_IdentityTransformer_Feat5_KNN,
   CombinedClassifier_IdentityTransformer_Feat5_RF,
   CombinedClassifier_IdentityTransformer_FeatAll_KNN,
   CombinedClassifier_IdentityTransformer_FeatAll_RF]
  

• Add the generated models to your pipeline:

  pipe.add_model(models, validation_method="2-fold")
  

Contributing

Your experience applying this toolset in your specialized field is extremely valuable. Any feedback and contributions are highly welcomed!

• Report bugs: Found an issue? Please open a GitHub issue with details
• Share your domain expertise: Tell us how SpecPipe works (or doesn't work) in your specific application area in discussions
• Suggest features: Have ideas for improvements? Use the GitHub discussions or issues tab
• Submit pull requests: Feel free to fork and submit PRs for bug fixes or small features
• Test and provide feedback: Try it out and let us know about your experience in discussions

License

This project is licensed under the MIT License - see the LICENSE file for details.