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Model/HICO/a9c810416e5f051290bee069f0aa75066950f5df6ceef624d1767bab1c45235b/Round_5/checkpoint rename to Model/HICO/28b743bfcdcbeb370dcfc2116fe46c44fb76f40090635c042e071c455545fe45/Round_5/checkpoint diff --git a/Model/HICO/28b743bfcdcbeb370dcfc2116fe46c44fb76f40090635c042e071c455545fe45/Round_5/model-10000.data-00000-of-00001.REMOVED.git-id b/Model/HICO/28b743bfcdcbeb370dcfc2116fe46c44fb76f40090635c042e071c455545fe45/Round_5/model-10000.data-00000-of-00001.REMOVED.git-id new file mode 100644 index 0000000..7487e12 --- /dev/null +++ b/Model/HICO/28b743bfcdcbeb370dcfc2116fe46c44fb76f40090635c042e071c455545fe45/Round_5/model-10000.data-00000-of-00001.REMOVED.git-id @@ -0,0 +1 @@ +568d3aef7b197cb9f0d82ec407eb0edc379786e9 \ No newline at end of file diff --git a/Model/HICO/28b743bfcdcbeb370dcfc2116fe46c44fb76f40090635c042e071c455545fe45/Round_5/model-10000.index b/Model/HICO/28b743bfcdcbeb370dcfc2116fe46c44fb76f40090635c042e071c455545fe45/Round_5/model-10000.index new file mode 100644 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D:/Data/Insitu +data_wavelengths : [501 507 512 518 524 530 535 541 547 553 558 564 570 575 581 587 593 598 + 604 610 616 621 627 633 638 644 650 656 661 667 673 679 684 690 696 701 + 707 713] +data_xtrain_shape : (4594, 38) +data_ytrain_shape : (4594, 1) +dataset : all +datasets_hash : e2c867501dd06d88056fc9ab9b44507e7d40c2a9726453080259dc035db3be5c +filename : None +model_loc : Model +n_redraws : 50 +n_rounds : 10 +no_load : False +no_save : False +plot_loss : False +save_data : False +save_stats : False +silent : False +sim_loc : D:/Data/Simulated +threshold : None +use_all_ratio : False +use_auc : False +use_boosting : False +use_excl_Rrs : False +use_kbest : 0 +use_noise : False +use_ratio : False +use_sim : False +use_tchlfix : False +verbose : False \ No newline at end of file diff --git a/Model/HICO/a9c810416e5f051290bee069f0aa75066950f5df6ceef624d1767bab1c45235b/Round_0/model-10000.data-00000-of-00001.REMOVED.git-id 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a/Model/HICO/a9c810416e5f051290bee069f0aa75066950f5df6ceef624d1767bab1c45235b/config b/Model/HICO/a9c810416e5f051290bee069f0aa75066950f5df6ceef624d1767bab1c45235b/config deleted file mode 100644 index e32e9ac..0000000 --- a/Model/HICO/a9c810416e5f051290bee069f0aa75066950f5df6ceef624d1767bab1c45235b/config +++ /dev/null @@ -1,50 +0,0 @@ -Created: 2021-01-19 14:59:54.293831 -Version: 1.1.0 --------------- -align: None -batch: 128 -benchmark: False -epsilon: 0.001 -imputations: 5 -l2: 0.001 -lr: 0.001 -model_lbl: -n_hidden: 100 -n_iter: 10000 -n_layers: 5 -n_mix: 5 -no_bagging: False -product: chl -sat_bands: False -seed: 42 -sensor: HICO -use_boosting: False -use_noise: False -use_ratio: False -use_tchlfix: False -x_scalers: [('RobustScaler', [], {})] -y_scalers: [('LogTransformer', [], {}), ('MinMaxScaler', [(-1, 1)], {})] --------------- -animate: False -avg_est: False -darktheme: False -data_loc: D:/Data/Test -data_wavelengths: [409, 415, 421, 426, 432, 438, 444, 449, 455, 461, 467, 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{}) + +Configuration +------------- +animate : False +avg_est : False +darktheme : False +data_loc : D:/Data/Insitu +data_wavelengths : [409 415 421 426 432 438 444 449 455 461 467 472 478 484 490 495 501 507 + 512 518 524 530 535 541 547 553 558 564 570 575 581 587 593 598 604 610 + 616 621 627 633 638 644 650 656 661 667 673 679 684 690 696 701 707 713 + 719 724 730 736 742 747 753 759 764 770 776 782 787] +data_xtrain_shape : (3626, 67) +data_ytrain_shape : (3626, 1) +dataset : all +datasets_hash : 0007a1169ff599afcb745dc48535bd3ab2435359884068ffac47ee112a9789db +filename : None +model_loc : Model +n_redraws : 50 +n_rounds : 10 +no_load : False +no_save : False +plot_loss : False +save_data : False +save_stats : False +silent : False +sim_loc : D:/Data/Simulated +threshold : None +use_all_ratio : False +use_auc : False +use_boosting : False +use_excl_Rrs : False +use_kbest : 0 +use_noise : False +use_ratio : False +use_sim : False +use_tchlfix : False +verbose : False \ No newline 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+product : chl +sat_bands : False +seed : 42 +sensor : OLCI-poly +x_scalers : + ('RobustScaler', [], {}) +y_scalers : + ('LogTransformer', [], {}) + ('MinMaxScaler', [(-1, 1)], {}) + +Configuration +------------- +animate : False +avg_est : False +darktheme : False +data_loc : D:/Data/Insitu +data_wavelengths : [411 442 490 510 560 619 664 681 708 753 778] +data_xtrain_shape : (3694, 11) +data_ytrain_shape : (3694, 1) +dataset : all +datasets_hash : 0007a1169ff599afcb745dc48535bd3ab2435359884068ffac47ee112a9789db +filename : None +model_loc : Model +n_redraws : 50 +n_rounds : 10 +no_load : False +no_save : False +plot_loss : False +save_data : False +save_stats : False +silent : False +sim_loc : D:/Data/Simulated +threshold : None +use_all_ratio : False +use_auc : False +use_boosting : False +use_excl_Rrs : False +use_kbest : 0 +use_noise : False +use_ratio : False +use_sim : False +use_tchlfix : False +verbose : False \ No newline at end of file diff --git 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/dev/null @@ -1,46 +0,0 @@ -Created: 2020-10-28 12:02:31.186645 -Version: 1.1.0 --------------- -align: None -batch: 128 -benchmark: False -epsilon: 0.001 -imputations: 5 -l2: 0.001 -lr: 0.001 -model_lbl: -n_hidden: 100 -n_iter: 10000 -n_layers: 5 -n_mix: 5 -no_bagging: False -product: chl -sat_bands: False -seed: 42 -sensor: OLCI-poly -use_boosting: False -use_noise: False -use_ratio: False -use_tchlfix: False -x_scalers: [('RobustScaler', [], {})] -y_scalers: [('LogTransformer', [], {}), ('MinMaxScaler', [(-1, 1)], {})] --------------- -animate: False -avg_est: False -darktheme: False -data_loc: D:/Data/Test -dataset: all -filename: None -model_loc: Model -n_redraws: 50 -n_rounds: 10 -no_load: False -no_save: False -plot_loss: False -save_data: False -save_stats: False -silent: False -sim_loc: D:/Data/Train -threshold: None -use_sim: False -verbose: False \ No newline at end of file diff --git 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+------------ +align : None +batch : 128 +benchmark : False +epsilon : 0.001 +imputations : 5 +l2 : 0.001 +lr : 0.001 +model_lbl : +n_hidden : 100 +n_iter : 10000 +n_layers : 5 +n_mix : 5 +no_bagging : False +product : chl +sat_bands : False +seed : 42 +sensor : OLCI +x_scalers : + ('RobustScaler', [], {}) +y_scalers : + ('LogTransformer', [], {}) + ('MinMaxScaler', [(-1, 1)], {}) + +Configuration +------------- +animate : False +avg_est : False +darktheme : False +data_loc : D:/Data/Insitu +data_wavelengths : [411 442 490 510 560 619 664 673 681 708 753 761 764 767 778] +data_xtrain_shape : (3628, 15) +data_ytrain_shape : (3628, 1) +dataset : all +datasets_hash : 0007a1169ff599afcb745dc48535bd3ab2435359884068ffac47ee112a9789db +filename : None +model_loc : Model +n_redraws : 50 +n_rounds : 10 +no_load : False +no_save : False +plot_loss : False +save_data : False +save_stats : False +silent : False +sim_loc : D:/Data/Simulated +threshold : None +use_all_ratio : False +use_auc : False 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a/Model/S2A/4cd6306659055702c13dbf1f69bec44028cd21237e65f3c0423225aa0b44b1ea/config +++ /dev/null @@ -1,46 +0,0 @@ -Created: 2020-10-27 17:06:03.915563 -Version: 1.1.0 --------------- -align: None -batch: 128 -benchmark: False -epsilon: 0.001 -imputations: 5 -l2: 0.001 -lr: 0.001 -model_lbl: -n_hidden: 100 -n_iter: 10000 -n_layers: 5 -n_mix: 5 -no_bagging: False -product: chl -sat_bands: False -seed: 42 -sensor: S2A -use_boosting: False -use_noise: False -use_ratio: False -use_tchlfix: False -x_scalers: [('RobustScaler', [], {})] -y_scalers: [('LogTransformer', [], {}), ('MinMaxScaler', [(-1, 1)], {})] --------------- -animate: False -avg_est: False -darktheme: False -data_loc: D:/Data/Test -dataset: all -filename: None -model_loc: Model -n_redraws: 50 -n_rounds: 10 -no_load: True -no_save: False -plot_loss: False -save_data: False -save_stats: False -silent: False -sim_loc: D:/Data/Train -threshold: None -use_sim: False -verbose: False \ No newline at end of file diff --git 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a/Model/S2B/f7f3032972de8dc8a7fee6bd260d8b9c1843d2e8a27a8783bd3d87f3fe6c0b27/Round_9/scaler.pkl b/Model/S2B/f7f3032972de8dc8a7fee6bd260d8b9c1843d2e8a27a8783bd3d87f3fe6c0b27/Round_9/scaler.pkl deleted file mode 100644 index da8b835..0000000 Binary files a/Model/S2B/f7f3032972de8dc8a7fee6bd260d8b9c1843d2e8a27a8783bd3d87f3fe6c0b27/Round_9/scaler.pkl and /dev/null differ diff --git a/Model/S2B/f7f3032972de8dc8a7fee6bd260d8b9c1843d2e8a27a8783bd3d87f3fe6c0b27/config b/Model/S2B/f7f3032972de8dc8a7fee6bd260d8b9c1843d2e8a27a8783bd3d87f3fe6c0b27/config deleted file mode 100644 index 4a593f2..0000000 --- a/Model/S2B/f7f3032972de8dc8a7fee6bd260d8b9c1843d2e8a27a8783bd3d87f3fe6c0b27/config +++ /dev/null @@ -1,46 +0,0 @@ -Created: 2020-10-27 16:59:40.311166 -Version: 1.1.0 --------------- -align: None -batch: 128 -benchmark: False -epsilon: 0.001 -imputations: 5 -l2: 0.001 -lr: 0.001 -model_lbl: -n_hidden: 100 -n_iter: 10000 -n_layers: 5 -n_mix: 5 -no_bagging: False -product: chl -sat_bands: False -seed: 42 -sensor: S2B -use_boosting: False -use_noise: False -use_ratio: False -use_tchlfix: False -x_scalers: [('RobustScaler', [], {})] -y_scalers: [('LogTransformer', [], {}), ('MinMaxScaler', [(-1, 1)], {})] --------------- -animate: False -avg_est: False -darktheme: False -data_loc: D:/Data/Test -dataset: all -filename: None -model_loc: Model -n_redraws: 50 -n_rounds: 10 -no_load: True -no_save: False -plot_loss: False -save_data: False -save_stats: False -silent: False -sim_loc: D:/Data/Train -threshold: None -use_sim: False -verbose: False \ No newline at end of file diff --git a/README.md b/README.md index d93ae6c..1a686a8 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,8 @@ This repository contains source code for the following papers: - ["Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach"](https://www.sciencedirect.com/science/article/pii/S0034425719306248). N. Pahlevan, et al. (2020). Remote Sensing of Environment. 111604. 10.1016/j.rse.2019.111604. - ["Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters"](https://www.sciencedirect.com/science/article/abs/pii/S0034425720301383). S.V. Balasubramanian, et al. (2020). Remote Sensing of Environment. 111768. 10.1016/j.rse.2020.111768. [Code](https://github.com/BrandonSmithJ/MDN/tree/master/Benchmarks/tss/SOLID). +- ["Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters"](https://www.sciencedirect.com/science/article/pii/S0034425720305733). N. Pahlevan, et al. (2021). Remote Sensing of Environment. 112200. 10.1016/j.rse.2020.112200. +- ["A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks"](https://www.frontiersin.org/articles/10.3389/frsen.2020.623678/full). B. Smith, et al. (2021). Frontiers in Remote Sensing. 623678. 10.3389/frsen.2020.623678.
### Usage @@ -21,7 +23,7 @@ Alternatively, you may use pip to install: The code may then either be used as a library, such as with the following: ``` from MDN import image_estimates, get_tile_data, get_sensor_bands -sensor = "" +sensor = "" # Tile should be the output of an atmospheric correction program e.g. SeaDAS bands, Rrs = get_tile_data("path/to/my/tile.nc", sensor, allow_neg=False) @@ -38,7 +40,7 @@ chlor_a = estimates[0] Or, a .csv file may be given as input, with each row as a single sample. The .csv contents should be only the Rrs values to be estimated on (i.e. no header row or index column). -`python3 -m MDN --sensor path/to/my/Rrs.csv` +`python3 -m MDN --sensor path/to/my/Rrs.csv` *Note:* The user-supplied input values should correspond to Rrs (units of 1/sr). diff --git a/__version__.py b/__version__.py index 6df8ed0..e916218 100644 --- a/__version__.py +++ b/__version__.py @@ -1 +1 @@ -__version__ = '1.1.0' \ No newline at end of file +__version__ = '1.2.0' \ No newline at end of file diff --git a/mdn.py b/mdn.py index 2ea5561..ff0a375 100644 --- a/mdn.py +++ b/mdn.py @@ -146,7 +146,7 @@ def fit(self, X, y, output_slices={'': slice(None)}, **kwargs): raise Exception('Model exists, but no_load is set and no training data was given.') elif X is not None and y is not None: - self.scalerx.fit( self._ensure_format(X) ) + self.scalerx.fit( self._ensure_format(X), self._ensure_format(y) ) self.scalery.fit( self._ensure_format(y) ) # Gather all data (train, validation, test, ...) into singular object @@ -174,6 +174,7 @@ def fit(self, X, y, output_slices={'': slice(None)}, **kwargs): self.graph.finalize() return self + @ignore_warnings def predict(self, X, confidence_interval=None, threshold=None): ''' diff --git a/parameters.py b/parameters.py index b05f5b0..9a0f8d2 100644 --- a/parameters.py +++ b/parameters.py @@ -1,7 +1,5 @@ -from .meta import SENSOR_LABEL import argparse - parser = argparse.ArgumentParser(epilog=""" Passing a filename will estimate the desired parameter from the Rrs contained in that file. Otherwise, a model will be trained (if not @@ -10,17 +8,17 @@ parser.add_argument("filename", nargs ="?", help="CSV file containing Rrs values to estimate from") parser.add_argument("--model_loc", default="Model", help="Location of trained models") -# parser.add_argument("--data_loc", default="/media/brandon/NASA/Data/Test", help="Location of in situ data") -# parser.add_argument("--sim_loc", default="/media/brandon/NASA/Data/Train", help="Location of simulated data") -parser.add_argument("--data_loc", default="D:/Data/Test", help="Location of in situ data") -parser.add_argument("--sim_loc", default="D:/Data/Train", help="Location of simulated data") +# parser.add_argument("--data_loc", default="/media/brandon/NASA/Data/Insitu", help="Location of in situ data") +# parser.add_argument("--sim_loc", default="/media/brandon/NASA/Data/Simulated", help="Location of simulated data") +parser.add_argument("--data_loc", default="D:/Data/Insitu", help="Location of in situ data") +parser.add_argument("--sim_loc", default="D:/Data/Simulated", help="Location of simulated data") parser.add_argument("--n_redraws", default=50, type=int, help="Number of plot redraws during training (i.e. updates plot every n_iter / n_redraws iterations); only used with --plot_loss.") parser.add_argument("--n_rounds", default=10, type=int, help="Number of models to fit, with median output as the final estimate") ''' Flags ''' -parser.add_argument("--threshold", default=None, type=float, help="Output the maximum prior estimate when the prior is above this threshold, and the weighted average estimate otherwise. Set to None, thresholding is not used.") -parser.add_argument("--avg_est", action ="store_true", help="Use the prior probability weighted mean as the estimate. Otherwise, use maximum prior.") +parser.add_argument("--threshold", default=None, type=float, help="Output the maximum prior estimate when the prior is above this threshold, and the weighted average estimate otherwise. Set to None, thresholding is not used") +parser.add_argument("--avg_est", action ="store_true", help="Use the prior probability weighted mean as the estimate; otherwise, use maximum prior") parser.add_argument("--no_save", action ="store_true", help="Do not save the model after training") parser.add_argument("--no_load", action ="store_true", help="Do load a saved model (and overwrite, if not no_save)") parser.add_argument("--verbose", action ="store_true", help="Verbose output printing") @@ -37,33 +35,41 @@ update.add_argument("--sat_bands", action ="store_true", help="Use bands specific to certain products when utilizing satellite retrieved spectra") update.add_argument("--benchmark", action ="store_true", help="Train only on partial dataset, and use remaining to benchmark") update.add_argument("--product", default="chl", help="Product to estimate") -update.add_argument("--sensor", default="OLI", help="Sensor to estimate from", choices=SENSOR_LABEL) -update.add_argument("--align", default=None, help="Comma-separated list of sensors to align data with. Passing \"all\" uses all sensors.", choices=['all']+list(SENSOR_LABEL)) +update.add_argument("--sensor", default="OLI", help="Sensor to estimate from (See meta.py for available options)") +update.add_argument("--align", default=None, help="Comma-separated list of sensors to align data with; passing \"all\" uses all sensors (See meta.py for available options)") update.add_argument("--model_lbl", default="", help="Label for a model") update.add_argument("--seed", default=42, type=int, help="Random seed") ''' Flags which have a yet undecided default value ''' -# update.add_argument("--no_noise", action ="store_true", help="Do not add noise when training the model") -update.add_argument("--use_noise", action ="store_true", help="Add noise when training the model") +flags = parser.add_argument_group('Model Flags', 'Flags which require a new model to be trained if they are changed') + +# flags.add_argument("--no_noise", action ="store_true", help="Do not add noise when training the model") +flags.add_argument("--use_noise", action ="store_true", help="Add noise when training the model") + +# flags.add_argument("--no_ratio", action ="store_true", help="Do not add band ratios as input features") +flags.add_argument("--use_ratio", action ="store_true", help="Add band ratios as input features") + +flags.add_argument("--use_auc", action ="store_true", help="Normalize input features using AUC") -# update.add_argument("--no_ratio", action ="store_true", help="Do not add band ratios as input features") -update.add_argument("--use_ratio", action ="store_true", help="Add band ratios as input features") +flags.add_argument("--use_tchlfix", action ="store_true", help="Correct chl for pheopigments") +# flags.add_argument("--no_tchlfix", action ="store_true", help="Do not correct chl for pheopigments") -update.add_argument("--use_tchlfix", action ="store_true", help="Correct chl for pheopigments") -# update.add_argument("--no_tchlfix", action ="store_true", help="Do not correct chl for pheopigments") +# flags.add_argument("--no_cache", action ="store_true", help="Do not use any cached data") +# flags.add_argument("--use_cache", action ="store_true", help="Use cached data, if available") -# parser.add_argument("--no_cache", action ="store_true", help="Do not use any cached data") -# parser.add_argument("--use_cache", action ="store_true", help="Use cached data, if available") +flags.add_argument("--use_boosting", action ="store_true", help="Use boosting when training in multiple trials (no longer implemented)") +# flags.add_argument("--no_boosting",action ="store_true", help="Do not use boosting when training in multiple trials") -update.add_argument("--use_boosting", action ="store_true", help="Use boosting when training in multiple trials") -# update.add_argument("--no_boosting",action ="store_true", help="Do not use boosting when training in multiple trials") +flags.add_argument("--no_bagging", action ="store_true", help="Do not use bagging when training in multiple trials") +# flags.add_argument("--use_bagging", action ="store_true", help="Use bagging when training in multiple trials") -update.add_argument("--no_bagging",action ="store_true", help="Do not use bagging when training in multiple trials") -# update.add_argument("--use_bagging", action ="store_true", help="Use bagging when training in multiple trials") +flags.add_argument("--use_sim", action ="store_true", help="Use simulated training data") +flags.add_argument("--use_excl_Rrs", action ="store_true", help="Drop raw Rrs features from the input when using ratio features") +flags.add_argument("--use_all_ratio", action ="store_true", help="Use exhaustive list of ratio features instead of only those found in literature (should be combined with --use_kbest)") -parser.add_argument("--use_sim", action ="store_true", help="Use simulated training data") +flags.add_argument("--use_kbest", type=int, nargs='?', const=5, default=0, help="Select top K features to use as input, based on mutual information") diff --git a/product_estimation.py b/product_estimation.py index 277e98f..4698c7e 100644 --- a/product_estimation.py +++ b/product_estimation.py @@ -11,7 +11,7 @@ from .plot_utils import plot_scatter from .benchmarks import run_benchmarks from .parameters import get_args -from .transformers import TransformerPipeline, LogTransformer, RatioTransformer, BaggingColumnTransformer +from .transformers import TransformerPipeline, generate_scalers def get_estimates(args, x_train=None, y_train=None, x_test=None, y_test=None, output_slices=None, dataset_labels=None): @@ -19,36 +19,10 @@ def get_estimates(args, x_train=None, y_train=None, x_test=None, y_test=None, ou Estimate all target variables for the given x_test. If a model doesn't already exist, creates a model with the given training data. ''' - wavelengths = get_sensor_bands(args.sensor, args) - store_scaler = lambda scaler, args=[], kwargs={}: (scaler, args, kwargs) - - # Note that additional x_scalers are added to the beginning of the pipeline (e.g. Robust( bagging( ratio(x) ) )) - args.x_scalers = [ - store_scaler(preprocessing.RobustScaler), - ] - args.y_scalers = [ - store_scaler(LogTransformer), - store_scaler(preprocessing.MinMaxScaler, [(-1, 1)]), - ] - - # We only want bagging to be applied to the columns if there are a large number of feature (e.g. ancillary features included) - many_features = any(x is not None and (x.shape[1]-len(wavelengths)) > 15 for x in [x_train, x_test]) - - # Add bagging to the columns (use a random subset of columns, excluding the first columns from the process) - if using_feature(args, 'bagging') and (using_feature(args, 'ratio') or many_features): - n_extra = 0 if not using_feature(args, 'ratio') else RatioTransformer.n_features # Number of ratio features added - args.x_scalers = [ - store_scaler(BaggingColumnTransformer, [len(wavelengths)], {'n_extra':n_extra}), - ] + args.x_scalers - - # Add additional features to the inputs - if using_feature(args, 'ratio'): - args.x_scalers = [ - store_scaler(RatioTransformer, [list(wavelengths)]), - ] + args.x_scalers + # Add x/y scalers to the args object + generate_scalers(args, x_train, x_test) # Add a few additional variables to be stored in the generated config file - setattr(args, 'data_wavelengths', list(wavelengths)) if x_train is not None: setattr(args, 'data_xtrain_shape', x_train.shape) if y_train is not None: setattr(args, 'data_ytrain_shape', y_train.shape) if x_test is not None: setattr(args, 'data_xtest_shape', x_test.shape) @@ -112,7 +86,7 @@ def get_estimates(args, x_train=None, y_train=None, x_test=None, y_test=None, ou # To speed up the process and limit memory consumption, apply the trained model to the given test data in chunks for i in trange(0, len(x_test), chunk_size, disable=not args.verbose): - est = model.predict(x_test[i:i+chunk_size], confidence_interval=None) + est = model.predict(x_test[i:i+chunk_size], confidence_interval=getattr(args, 'CI', None)) partial_est.append( np.array(est, ndmin=3) ) estimates.append( np.hstack(partial_est) ) @@ -120,7 +94,7 @@ def get_estimates(args, x_train=None, y_train=None, x_test=None, y_test=None, ou if args.verbose and y_test is not None: median = np.median(np.stack(estimates, axis=1)[0], axis=0) - labels = get_labels(wavelengths, output_slices, n_out=y_test.shape[1]) + labels = get_labels(args.data_wavelengths, output_slices, n_out=y_test.shape[1]) for lbl, y1, y2 in zip(labels, y_test.T, median.T): print( performance(f'{lbl:>7s} Median', y1, y2) ) print(f'--- Done round {round_num} ---\n') @@ -129,6 +103,8 @@ def get_estimates(args, x_train=None, y_train=None, x_test=None, y_test=None, ou # estimates) if a confidence_interval within (0,1) is passed into model.predict if x_test is not None: estimates, *confidence_bounds = np.stack(estimates, axis=1) + if len(confidence_bounds): + return estimates, model.output_slices, confidence_bounds return estimates, model.output_slices @@ -193,29 +169,42 @@ def apply_model(x_test, use_cmdline=True, **kwargs): return np.median(preds, 0), idxs +def print_dataset_stats(**kwargs): + ''' Print datasets shape & min / max stats per feature ''' + label = kwargs.pop('label', '') + for k, arr in kwargs.items(): + print(f'\n{label} {k.title()}'.strip()+'\n\t'.join(['']+[f'{k}: {v}' for k, v in { + 'Shape' : arr.shape, + 'N Valid' : getattr(np.isfinite(arr).sum(0), 'min' if arr.shape[1] > 10 else 'tolist')(), + 'Min,Max' : list(zip(np.nanmin(arr, 0).round(2), np.nanmax(arr, 0).round(2))), + }.items()])) + + def main(): args = get_args() # If a file was given, estimate the product for the Rrs contained within if args.filename: filename = Path(args.filename) - assert(filename.exists()), ( - f'Expecting path to in situ data as the passed argument, but "{filename}" does not exist.') + assert(filename.exists()), f'Expecting "{filename}" to be path to Rrs data, but it does not exist.' + + bands = get_sensor_bands(args.sensor, args) + if filename.is_file(): x_test = np.loadtxt(args.filename, delimiter=',') + else: x_test, *_ = _load_datasets(['Rrs'], [filename], bands) - x_test = np.loadtxt(args.filename, delimiter=',') - print(f'Min Rrs: {x_test.min(0)}') - print(f'Max Rrs: {x_test.max(0)}') print(f'Generating estimates for {len(x_test)} data points ({x_test.shape})') - preds, idxs = get_estimates(args, x_test=x_test) - print(f'Min: {np.median(preds, 0).min(0)}') - print(f'Max: {np.median(preds, 0).max(0)}') + print_dataset_stats(rrs=x_test, label='Input') - labels = get_labels(get_sensor_bands(args.sensor, args), idxs, preds[0].shape[1]) - preds = np.append(np.array(labels)[None,:], np.median(preds, 0), 0) + estimates, slices = get_estimates(args, x_test=x_test) + estimates = np.median(estimates, 0) + print_dataset_stats(estimates=estimates, label='MDN') - filename = filename.parent.joinpath(f'MDN_{filename.stem}.csv').as_posix() + labels = get_labels(bands, slices, estimates.shape[1]) + estimates = np.append([labels], estimates, 0).astype(str) + filename = filename.parent.joinpath(f'MDN_{filename.stem}.csv').as_posix() + print(f'Saving estimates at location "{filename}"') - np.savetxt(filename, preds.astype(str), delimiter=',', fmt='%s') + np.savetxt(filename, estimates, delimiter=',', fmt='%s') # Save data used with the given args elif args.save_data: @@ -224,13 +213,14 @@ def main(): valid = np.any(np.isfinite(x_data), 1) x_data = x_data[valid].astype(str) y_data = y_data[valid].astype(str) - locs = np.array(locs).T[valid].astype(str) + locs = np.array(locs)[valid].astype(str) wvls = list(get_sensor_bands(args.sensor, args).astype(int).astype(str)) lbls = get_labels(get_sensor_bands(args.sensor, args), slices, y_data.shape[1]) data = np.append([wvls], x_data.astype(str), 0) data_full = np.append(np.append(locs, x_data, 1), y_data, 1) - data_full = np.append([['index', 'dataset']+wvls+lbls], data_full, 0) + data_full = np.append([['dataset', 'index']+wvls+lbls], data_full, 0) np.savetxt(f'{args.sensor}_data_full.csv', data_full, delimiter=',', fmt='%s') + print(f'Saved data with shape {data_full.shape}') # Train a model with partial data, and benchmark on remaining elif args.benchmark: diff --git a/transformers.py b/transformers.py index 701101e..2e13296 100644 --- a/transformers.py +++ b/transformers.py @@ -1,5 +1,11 @@ +from sklearn.feature_selection import SelectKBest, mutual_info_regression from sklearn.base import TransformerMixin from sklearn import preprocessing +from itertools import combinations + +from .meta import get_sensor_bands +from .utils import using_feature + import pickle as pkl import numpy as np import warnings @@ -13,41 +19,39 @@ class CustomTransformer(TransformerMixin): def fit(self, X, *args, **kwargs): self._input_shape = X.shape[1] - return self._fit(X.copy(), *args, **kwargs) + self._fit(X.copy(), *args, **kwargs) + return self def transform(self, X, *args, **kwargs): - if self._input_shape is not None: - assert(X.shape[1] == self._input_shape), f'Number of data features changed: {self._input_shape} vs {X.shape[1]}' + self._validate_shape(X, self._input_shape) X = self._transform(X.copy(), *args, **kwargs) - - if self._output_shape is not None: - assert(X.shape[1] == self._output_shape), f'Number of data features changed: {self._output_shape} vs {X.shape[1]}' + self._validate_shape(X, self._output_shape) self._output_shape = X.shape[1] return X def inverse_transform(self, X, *args, **kwargs): - if self._output_shape is not None: - assert(X.shape[1] == self._output_shape), f'Number of data features changed: {self._output_shape} vs {X.shape[1]}' + self._validate_shape(X, self._output_shape) X = self._inverse_transform(X.copy(), *args, **kwargs) - - if self._input_shape is not None: - assert(X.shape[1] == self._input_shape), f'Number of data features changed: {self._input_shape} vs {X.shape[1]}' + self._validate_shape(X, self._input_shape) self._input_shape = X.shape[1] return X - def _fit(self, X, *args, **kwargs): return self - def _transform(self, X, *args, **kwargs): raise NotImplemented + @staticmethod + def config_info(*args, **kwargs): return '' # Return any additional info to construct model config + def _fit(self, X, *args, **kwargs): pass + def _transform(self, X, *args, **kwargs): raise NotImplemented def _inverse_transform(self, X, *args, **kwargs): raise NotImplemented - + def _validate_shape(self, X, shape): assert(shape is None or X.shape[1] == shape), \ + f'Number of data features changed: expected {shape}, found {X.shape[1]}' class IdentityTransformer(CustomTransformer): - def _transform(self, X, *args, **kwargs): return X + def _transform(self, X, *args, **kwargs): return X def _inverse_transform(self, X, *args, **kwargs): return X class LogTransformer(CustomTransformer): - def _transform(self, X, *args, **kwargs): return np.log(X) + def _transform(self, X, *args, **kwargs): return np.log(X) def _inverse_transform(self, X, *args, **kwargs): return np.exp(X) @@ -56,18 +60,28 @@ class NegLogTransformer(CustomTransformer): Log-like transformation which allows negative values (Whittaker et al. 2005) http://fmwww.bc.edu/repec/bocode/t/transint.html ''' - def _transform(self, X, *args, **kwargs): return np.sign(X) * np.log(np.abs(X) + 1) + def _transform(self, X, *args, **kwargs): return np.sign(X) * np.log(np.abs(X) + 1) def _inverse_transform(self, X, *args, **kwargs): return np.sign(X) * (np.exp(np.abs(X)) - 1) class ColumnTransformer(CustomTransformer): ''' Reduce columns to specified selections (feature selection) ''' - def __init__(self, columns, *args, **kwargs): self._c = columns - def _transform(self, X, *args, **kwargs): return X[:, self._c] + def __init__(self, columns, *args, **kwargs): self._c = columns + def _transform(self, X, *args, **kwargs): return X[:, self._c] + + +class AUCTransformer(CustomTransformer): + ''' Area under the curve normalization ''' + def __init__(self, wavelengths, *args, **kwargs): self.wavelengths = wavelengths + def _transform(self, X, *args, **kwargs): return X/np.trapz(X, self.wavelengths, axis=1)[:, None] class BaggingColumnTransformer(CustomTransformer): - ''' Randomly select a percentage of columns to drop ''' + ''' + Randomly select a percentage of columns to drop, always keeping the + band features (first n_bands columns). Optionally, the last n_extra + columns will also be always kept as additional features. + ''' percent = 0.75 def __init__(self, n_bands, *args, n_extra=0, **kwargs): @@ -75,32 +89,14 @@ def __init__(self, n_bands, *args, n_extra=0, **kwargs): self.n_extra = n_extra def _fit(self, X, *args, **kwargs): - # if X.shape[1] > 60: - # self.percent = 0.05 - # n_bands_tmp = self.n_bands - # self.n_bands = 27 - shp = X.shape[1] - self.n_bands ncol = int(shp*self.percent) cols = np.arange(shp-self.n_extra) + self.n_bands np.random.shuffle(cols) - - # if X.shape[1] > 60: - # shp2 = self.n_bands - n_bands_tmp - # ncol2 = int(shp2*0.75) - # cols2 = np.arange(shp2) + n_bands_tmp - # np.random.shuffle(cols2) - # self.cols = np.append(np.arange(n_bands_tmp), cols2) - # self.cols = np.append(self.cols, cols[:ncol]) - # ncol += ncol2 - # else: - - if self.n_extra: - self.cols = np.append(np.arange(self.n_bands), list(cols[:ncol]) + list(X.shape[1]-(np.arange(self.n_extra)+1)), 0) - else: - self.cols = np.append(np.arange(self.n_bands), list(cols[:ncol]), 0) + new_cols = list(cols[:ncol]) + if self.n_extra: new_cols += list(X.shape[1]-(np.arange(self.n_extra)+1)) + self.cols = np.append(np.arange(self.n_bands), new_cols, 0) # print(f'Reducing bands from {shp} ({X.shape[1]} total) to {ncol} ({len(self.cols)} total) ({self.cols})') - return self def _transform(self, X, *args, **kwargs): return X[:, self.cols.astype(int)] @@ -122,7 +118,7 @@ def __init__(self, exclude_slice, transformer, transformer_args=[], transformer_ self.excl = exclude_slice self.transformer = transformer(*transformer_args, **transformer_kwargs) - def _fit(self, X): + def _fit(self, X, *args, **kwargs): cols = np.arange(X.shape[1]) cols = [c for c in cols if c not in cols[self.excl]] self.transformer.fit(X[:, cols]) @@ -145,126 +141,109 @@ def _inverse_transform(self, X, *args, **kwargs): class RatioTransformer(CustomTransformer): ''' Add ratio features ''' - n_features = 3 # Should be changed to match the number of features which are created in _transform - - def __init__(self, wavelengths, *args, label='', **kwargs): + def __init__(self, wavelengths, *args, excl_Rrs=False, all_ratio=False, **kwargs): + assert(not all_ratio or len(wavelengths) < 16), 'Too many features would be created with > 15 bands (i.e. > 1000 features)' self.wavelengths = list(wavelengths) - self.label = label + self.all_ratio = all_ratio + self.excl_Rrs = excl_Rrs + self.labels = [] + + @staticmethod + def config_info(*args, **kwargs): + transformer = RatioTransformer(*args, **kwargs) + return f'{transformer.get_n_features()} features: {transformer.labels}' - def _fit(self, X): + def get_n_features(self): + random_data = np.ones((2, len(self.wavelengths))) + return self._transform(random_data).shape[-1] + + def _fit(self, X, *args, **kwargs): self.shape = X.shape[1] - return self def _transform(self, X, *args, **kwargs): - ''' - Simple feature engineering method. Add band - ratios as features. Does not add reciprocal - ratios or any other duplicate features; - adds a band sum ratio (based on three-band - Chl retrieval method). - - Usage: - # one sample with three features, shaped [samples, features] - x = [[a, b, c]] - y = ratio(x) - # y -> [[a, b, c, b/a, b/(a+c), c/a, c/(a+b), c/b, a/(b+c)] - ''' - - def LH(L1, L2, L3, R1, R2, R3): - c = (L3 - L2) / (L3 - L1) - return R2 - c*R1 - (1-c)*R3 + ''' Ratios based on literature ''' + from .Benchmarks.utils import get_required, has_band, closest_wavelength + self.labels = [] x = np.atleast_2d(X) - x_new = [v for v in x.T] - label = [] - - R443 = x[:, self.wavelengths.index(443)] - R490 = x[:, self.wavelengths.index(490)] - R560 = x[:, self.wavelengths.index(560)] - R665 = x[:, self.wavelengths.index(665)] - R705 = x[:, self.wavelengths.index(705)] - R740 = x[:, self.wavelengths.index(740)] - R783 = x[:, self.wavelengths.index(783)] - R865 = x[:, self.wavelengths.index(865)] - # R1613 = x[:, self.wavelengths.index(1613)] + x_new = [] + Rrs = get_required(x, self.wavelengths, tol=11) + + def BR2(L1, L2): + self.labels.append(f'{L1}/{L2}') + return [Rrs(L1) / Rrs(L2)] + + def BR3(L1, L2, L3): + self.labels.append(f'{L2}/{L1}-{L2}/{L3}') + return [Rrs(L2)/Rrs(L1) - Rrs(L2)/Rrs(L3)] + + def LH(L1, L2, L3): + self.labels.append(f'{L1}-{L2}-{L3}') + c = (L3 - L2) / (L3 - L1) + return [Rrs(L2) - c*Rrs(L1) - (1-c)*Rrs(L3)] + + def PCA(n_components=1): + from sklearn.decomposition import PCA as PCA_sklearn + values = PCA_sklearn(n_components).fit_transform( Rrs(None) ) + self.labels += [f'PCA{i}' for i in range(values.shape[-1])] + return [values[:, i] for i in range(values.shape[-1])] + + features = ( + [(BR2, wvl) for wvl in combinations(self.wavelengths, 2)] + + [(BR3, wvl) for wvl in combinations(self.wavelengths, 3)] + + [(LH, wvl) for wvl in combinations(self.wavelengths, 3)] + ) if self.all_ratio else [ + (BR2, (745, 783)), + (BR2, (665, 705)), + (BR2, (560, 705)), + (BR2, (560, 665)), + (BR2, (490, 560)), + (BR2, (443, 490)), + (BR2, (665, 620)), + (BR2, (620, 560)), + + (LH, (490, 560, 665)), + (LH, (665, 705, 745)), + (LH, (705, 745, 783)), + (LH, (560, 620, 665)), + ] + + for function, wvls in features: + if all(has_band(wvl, self.wavelengths, tol=11) for wvl in wvls): + x_new += function(*closest_wavelength(wvls, self.wavelengths, tol=11)) - self.labels = [] - # x_new.append(R865 / R560); self.labels.append('865|560') # 2band - x_new.append(R705 / R665); self.labels.append('705|665') # 2band - x_new.append(R705 / R560); self.labels.append('705|560') # 2band - # x_new.append(R665 / R560); self.labels.append('665|560') # 2band - x_new.append(R560 / R490); self.labels.append('560|490') # 2band - # x_new.append(LH(783, 865, 1613, R783, R865, R1613)); self.labels.append('783-865-1613') # x_new.append((R705 - R665) / (R705 + R665)); self.labels.append('NDCI') # NDCI # x_new.append((R560 - R1613) / (R560 + R1613)); self.labels.append('MNDWI') # NDWI # x_new.append((1/R665 - 1/R705) / (1/R783 - 1/R705)); self.labels.append('YangBI') # Yang band index - # print('here:',len(x_new)) - assert(self.n_features == len(self.labels)), f'Need to change n_features from {self.n_features} to {len(self.labels)}: {self.labels}' - return np.hstack([v[:,None] for v in x_new]) - - - def transform2(self, X): - x = np.atleast_2d(X) - x_new = [v for v in x.T] - label = [] - # Band ratios - # if len(self.wavelengths) < 6: - for i, L1 in enumerate(self.wavelengths): - for j, L2 in enumerate(self.wavelengths): - if L1 < L2:# and abs(i-j) <= 2: - R1 = x[:, i] - R2 = x[:, j] - # if len(self.wavelengths) < 6 or (L1 > 500 and L2 > 500): - x_new.append(R2 / R1) - label.append(f'{self.label}{L2}/{L1}') - # print(f'{L2}/{L1}', np.min(x_new[-1]), np.max(x_new[-1])) - - for k, L3 in enumerate(self.wavelengths): - R3 = x[:, k] - - # if L2 < L3: - # x_new.append(R3 * (1/R1 - 1/R2)) - # label.append(f'{self.label}{L3}*(1/{L1}-1/{L2})') - - if L3 not in [L1, L2]: - - if L1 < L3: - x_new.append(R2 * (1/R1 - 1/R3)) - label.append(f'{self.label}{L2}*(1/{L1}-1/{L3})') - - # x_new.append(R2 / (R1 + R3)) - # label.append(f'{self.label}{L2}/({L1}+{L3})') - else: - x_new.append(R3 * (1/R1 - 1/R2)) - label.append(f'{self.label}{L3}*(1/{L1}-1/{L2})') - - # x_new.append(R3 / (R1 + R2)) - # label.append(f'{self.label}{L3}/({L1}+{L2})') - - - # Line height variations, examining height of center between two shoulder bands - for i, L1 in enumerate(self.wavelengths): - for j, L2 in enumerate(self.wavelengths): - for k, L3 in enumerate(self.wavelengths): - if (L3 > L2) and (L2 > L1):# and abs(i-j) <= 1 and abs(j-k) <= 1 and L2 < 900: - # if len(self.wavelengths) < 6:# or not (L1 == 655 and L2 == 865 and L3 == 1609): #L1 > 600:# and L2 600 and L3 > 600): - # if L2 < 600:# and L2 < 730: - c = (L3 - L2) / (L3 - L1) - R1 = x[:, i] - R2 = x[:, j] - R3 = x[:, k] - x_new.append(R2 - c*R1 - (1-c)*R3) - label.append(f'{self.label}({L2}-a{L1}-b{L3})') - # print(f'{len(self.wavelengths)} ({L3}-{L2})/({L3}-{L1})', np.min(x_new[-1]), np.max(x_new[-1])) - - self.labels = label - return np.hstack([v[:,None] for v in x_new]) + for xv in x_new: assert(np.all(np.isfinite(xv))), X[~np.isfinite(xv)] + assert(len(x_new) == len(self.labels)), f'Mismatch between features and labels: {len(x_new)} vs {len(self.labels)}' + if self.excl_Rrs: + return np.hstack([v.flatten()[:, None] for v in x_new]) + return np.append(x, np.hstack([v.flatten()[:, None] for v in x_new]), -1) def _inverse_transform(self, X, *args, **kwargs): return np.array(X)[:, :self.shape] +class KBestTransformer(CustomTransformer): + ''' Select the top K features, based on mutual information with the target variable. + When multiple target variables exist, each target variable receives an even share + of the features (i.e. 3 targets, K=6 -> K=2 for each target) ''' + def __init__(self, n_features, *args, **kwargs): + self.n_features = n_features + + def _fit(self, X, y, *args, **kwargs): + self.k_transformer = [] + for i in range(y.shape[-1]): + valid = np.isfinite(y[..., i]) + self.k_transformer.append( SelectKBest(mutual_info_regression, k=self.n_features // y.shape[-1]) ) + self.k_transformer[-1].fit(X[valid], y[valid, i]) + return self + + def _transform(self, X, *args, **kwargs): + return X[..., sorted(np.unique([np.argsort(t.scores_)[-t.k:] for t in self.k_transformer]))] + class TanhTransformer(CustomTransformer): ''' tanh-estimator (Hampel et al. 1986; Latha & Thangasamy, 2011) ''' @@ -299,19 +278,11 @@ def _inverse_transform(self, X, *args, **kwargs): return ((np.tan(X * 2 - 1) / self.scale) * self.sig_gh) + self.mu_gh - class TransformerPipeline(CustomTransformer): ''' Apply multiple transformers seamlessly ''' - def __init__(self, scalers=None): - if scalers is None or len(scalers) == 0: - self.scalers = [ - LogTransformer(), - preprocessing.RobustScaler(), - preprocessing.MinMaxScaler((-1, 1)), - ] - else: - self.scalers = scalers + def __init__(self, scalers=[]): + self.scalers = scalers def _fit(self, X, *args, **kwargs): for scaler in self.scalers: @@ -335,7 +306,6 @@ def fit_transform(self, X, *args, **kwargs): return X - class CustomUnpickler(pkl.Unpickler): ''' Ensure the classes are found, without requiring an import ''' _warned = False @@ -357,3 +327,50 @@ def load(self, *args, **kwargs): warnings.warn(w[0].message, w[0].category) CustomUnpickler._warned = True return pickled_object + + +def generate_scalers(args, x_train=None, x_test=None, column_bagging=False): + ''' Add scalers to the args object based on the contained parameter settings ''' + wavelengths = get_sensor_bands(args.sensor, args) + store_scaler = lambda scaler, args=[], kwargs={}: (scaler, args, kwargs) + setattr(args, 'data_wavelengths', wavelengths) + + # Note that the scaler list is applied in order, e.g. MinMaxScaler( LogTransformer(y) ) + args.x_scalers = [ + store_scaler(preprocessing.RobustScaler), + ] + args.y_scalers = [ + store_scaler(LogTransformer), + store_scaler(preprocessing.MinMaxScaler, [(-1, 1)]), + ] + + # We only want bagging to be applied to the columns if there are a large number of extra features (e.g. ancillary features included) + many_features = column_bagging and any(x is not None and (x.shape[1]-len(wavelengths)) > 15 for x in [x_train, x_test]) + + # Add bagging to the columns (use a random subset of columns, excluding the first columns from the process) + if column_bagging and using_feature(args, 'bagging') and (using_feature(args, 'ratio') or many_features): + n_extra = 0 if not using_feature(args, 'ratio') else RatioTransformer(wavelengths).get_n_features() # Number of ratio features added + args.x_scalers = [ + store_scaler(BaggingColumnTransformer, [len(wavelengths)], {'n_extra':n_extra}), + ] + args.x_scalers + + # Feature selection via mutual information + if using_feature(args, 'kbest'): + args.x_scalers = [ + store_scaler(KBestTransformer, [args.use_kbest]), + ] + args.x_scalers + + # Add additional features to the inputs + if using_feature(args, 'ratio'): + kwargs = {} + if using_feature(args, 'excl_Rrs'): kwargs.update({'excl_Rrs' : True}) + if using_feature(args, 'all_ratio'): kwargs.update({'all_ratio' : True}) + args.x_scalers = [ + store_scaler(RatioTransformer, [list(wavelengths)], kwargs), + ] + args.x_scalers + + # Normalize input features using AUC + if using_feature(args, 'auc'): + args.x_scalers = [ + store_scaler(AUCTransformer, [list(wavelengths)]), + ] + args.x_scalers diff --git a/utils.py b/utils.py index 45bf39c..9343c8c 100644 --- a/utils.py +++ b/utils.py @@ -1,6 +1,5 @@ -from .transformers import CustomUnpickler, RatioTransformer, ColumnTransformer from .meta import get_sensor_bands, SENSOR_BANDS, ANCILLARY, PERIODIC -from .parameters import update, hypers +from .parameters import update, hypers, flags from .__version__ import __version__ from collections import defaultdict as dd @@ -25,13 +24,16 @@ def helper(*args, **kwargs): def find_wavelength(k, waves, validate=True, tol=5): ''' Index of closest wavelength ''' - i = np.abs(np.array(waves) - k).argmin() - assert(not validate or (abs(k-waves[i]) <= tol)), f'Needed {k}nm, but closest was {waves[i]}nm in {waves}' - return i + waves = np.array(waves) + w = np.atleast_1d(k) + i = np.abs(waves - w[:, None]).argmin(1) + assert(not validate or (np.abs(w-waves[i]).max() <= tol)), f'Needed {k}, but closest was {waves[i]} in {waves} ({np.abs(w-waves[i]).max()} > {tol})' + return i.reshape(np.array(k).shape) def closest_wavelength(k, waves, validate=True, tol=5): ''' Value of closest wavelength ''' + waves = np.array(waves) return waves[find_wavelength(k, waves, validate, tol)] @@ -91,6 +93,7 @@ def store_pkl(filename, output): def read_pkl(filename): ''' Helper to read pickle file ''' + from .transformers import CustomUnpickler with Path(filename).open('rb') as f: return CustomUnpickler(f).load() @@ -120,6 +123,7 @@ def using_feature(args, flag): becomes signal = using_feature(args, 'ratio') # if true, we add ratios ''' + flag = flag.replace('use_', '').replace('no_', '') assert(hasattr(args,f'use_{flag}') or hasattr(args, f'no_{flag}')), f'"{flag}" flag not found' return getattr(args, f'use_{flag}', False) or not getattr(args, f'no_{flag}', True) @@ -240,6 +244,12 @@ def get_tile_data(filenames, sensor, allow_neg=True, rhos=False, anc=False): return bands, np.dstack([data[f] for f in features]) + + + + + + def generate_config(args, create=True, verbose=True): ''' Create a config file for the current settings, and store in @@ -256,20 +266,32 @@ def generate_config(args, create=True, verbose=True): if args.verbose: print(f'Using manually set model hash: {args.model_hash}') return root.joinpath(args.model_hash) + # Hash is always dependent upon these values dependents = [getattr(act, 'dest', '') for group in [hypers, update] for act in group._group_actions] dependents+= ['x_scalers', 'y_scalers'] - config = [f'Version: {__version__}'] + # Hash is only partially dependent upon these values, assuming operation changes when using a feature + # - 'use_' flags being set cause dependency + # - 'no_' flags being set remove dependency + # This allows additional flags to be added without breaking prior model compatibility + partials = [getattr(act, 'dest', '') for group in [flags] for act in group._group_actions] + + config = [f'Version: {__version__}', '', 'Dependencies'] config+= [''.join(['-']*len(config[-1]))] - others = [''.join(['-']*len(config[-1]))] + others = ['', 'Configuration'] + others+= [''.join(['-']*len(others[-1]))] for k,v in sorted(args.__dict__.items(), key=lambda z: z[0]): - if k in ['x_scalers', 'y_scalers']: - v = [(s[0].__name__,)+s[1:] for s in v] # stringify scaler and its arguments + if k in ['x_scalers', 'y_scalers']: + cinfo = lambda s, sarg, skw: getattr(s, 'config_info', lambda *a, **k: '')(*sarg, **skw) + cfmt = lambda *cargs: f' # {cinfo(*cargs)}' if cinfo(*cargs) else '' + v = '\n\t' + '\n\t'.join([f'{(s[0].__name__,) + s[1:]}{cfmt(*s)}' for s in v]) # stringify scaler and its arguments + + if k in partials and using_feature(args, k): + config.append(f'{k:<18}: {v}') + elif k in dependents: config.append(f'{k:<18}: {v}') + else: others.append(f'{k:<18}: {v}') - if k in dependents: config.append(f'{k}: {v}') - else: others.append(f'{k}: {v}') - config = '\n'.join(config) # Model is dependent on some arguments, so they change the hash others = '\n'.join(others) # Other arguments are stored for replicability ver_re = r'(Version\: \d+\.\d+)(?:\.\d+\n[-]+\n)' # Match major/minor version within subgroup, patch/dashes within pattern @@ -314,65 +336,76 @@ def _load_datasets(keys, locs, wavelengths, allow_missing=False): [bbp443, bbp483, bbp561, bbp655, chl], {'bbp':slice(0,4), 'chl':slice(4,5)} ''' - def loadtxt(name, loc, _wavelengths): + def loadtxt(name, loc, required_wvl): ''' Error handling wrapper over np.loadtxt, with the addition of wavelength selection''' dloc = Path(loc).joinpath(f'{name}.csv') - + # TSS / TSM are synonymous if 'tss' in name and not dloc.exists(): dloc = Path(loc).joinpath(f'{name.replace("tss","tsm")}.csv') - # CDOM is just an alias for a_cdom(443) + # CDOM is just an alias for a_cdom(443) or a_g(443) if 'cdom' in name and not dloc.exists(): dloc = Path(loc).joinpath('ag.csv') + required_wvl = [443] try: + required_wvl = np.array(required_wvl).flatten() assert(dloc.exists()), (f'Key {name} does not exist at {loc} ({dloc})') + data = np.loadtxt(dloc, delimiter=',', dtype=float if name not in ['../Dataset', '../meta'] else str, comments=None) - if len(data.shape) == 1: - data = data[:, None] + if len(data.shape) == 1: data = data[:, None] if data.shape[1] > 1 and data.dtype.type is not np.str_: # If we want to get all data, regardless of if bands are available... if allow_missing: - new_data = [[np.nan]*len(data)] * len(_wavelengths) - wvls = np.loadtxt(Path(loc).joinpath(f'{name}_wvl.csv'), delimiter=',')[:,None] - idxs = np.abs(wvls - np.atleast_2d(_wavelengths)).argmin(0) - valid = np.abs(wvls - np.atleast_2d(_wavelengths)).min(0) < 2 + new_data = [[np.nan]*len(data)] * len(required_wvl) + wvls = np.loadtxt(Path(loc).joinpath(f'{dloc.stem}_wvl.csv'), delimiter=',')[:,None] + idxs = np.abs(wvls - np.atleast_2d(required_wvl)).argmin(0) + valid = np.abs(wvls - np.atleast_2d(required_wvl)).min(0) < 2 for j, (i, v) in enumerate(zip(idxs, valid)): - # print(j, i, v) if v: new_data[j] = data[:, i] data = np.array(new_data).T else: - data = data[:, get_valid(name, loc, _wavelengths)] + data = data[:, get_valid(dloc.stem, loc, required_wvl)] if 'cdom' in name and dloc.stem == 'ag': - data = data[:, find_wavelength(443, np.loadtxt(Path(loc).joinpath(f'{dloc.stem}_wvl.csv'), delimiter=','))].flatten()[:, None] + data = data[:, find_wavelength(443, required_wvl)].flatten()[:, None] return data except Exception as e: - # assert(0),e - if dloc.exists(): - print(f'Error fetching {name} from {loc}: {e}') if name not in ['Rrs']:# ['../chl', '../tss', '../cdom']: + if dloc.exists(): + print(f'\n\tError fetching {name} from {loc}:\n{e}') return np.array([]).reshape((0,0)) - assert(0), e + raise e - def get_valid(name, loc, _wavelengths, margin=2): - ''' Dataset at must have all bands in <_wavelengths> within nm ''' + def get_valid(name, loc, required_wvl, margin=2): + ''' Dataset at must have all bands in within nm ''' if 'HYPER' in str(loc): margin=1 - wvls = np.loadtxt(Path(loc).joinpath(f'{name}_wvl.csv'), delimiter=',')[:,None] - assert(np.all([np.abs(wvls-w).min() <= margin for w in _wavelengths])), ( - f'{loc} is missing wavelengths: \n{_wavelengths} needed,\n{wvls.flatten()} found') - - if len(wvls) != len(_wavelengths): - valid = np.abs(wvls - np.atleast_2d(_wavelengths)).min(1) < margin - assert(valid.sum() == len(_wavelengths)), [wvls[valid].flatten(), _wavelengths] - return valid - return np.array([True] * len(_wavelengths)) - + # First, validate all required wavelengths are within the margin of an available wavelength + wvls = np.loadtxt(Path(loc).joinpath(f'{name}_wvl.csv'), delimiter=',')[:,None] + check = np.array([np.abs(wvls-w).min() <= margin for w in required_wvl]) + assert(check.all()), '\n\t\t'.join([ + f'{name} is missing {(~check).sum()} wavelengths:', + f'Needed {required_wvl}', f'Found {wvls.flatten()}', + f'Missing {required_wvl[~check]}', '']) + + # First, validate available wavelengths are within the margin of the required wavelengths + valid = np.array([True] * len(required_wvl)) + if len(wvls) != len(required_wvl): + valid = np.abs(wvls - np.atleast_2d(required_wvl)).min(1) < margin + assert(valid.sum() == len(required_wvl)), [wvls[valid].flatten(), required_wvl] + + # Then, ensure the order of the available wavelengths are the same as the required + if not all([w1 == w2 for w1,w2 in zip(wvls[valid], required_wvl)]): + valid = [np.abs(wvls.flatten() - w).argmin() for w in required_wvl] + assert(len(np.unique(valid)) == len(valid) == len(required_wvl)), [valid, wvls[valid].flatten(), required_wvl] + return valid + + locs = [Path(loc).resolve() for loc in np.atleast_1d(locs)] print('\n-------------------------') print(f'Loading data for sensor {locs[0].parts[-1]}') if allow_missing: @@ -381,13 +414,13 @@ def get_valid(name, loc, _wavelengths, margin=2): x_data = [] y_data = [] l_data = [] - for loc in np.atleast_1d(locs): + for loc in locs: try: loc_data = [loadtxt(key, loc, wavelengths) for key in keys] print(f'\tN={len(loc_data[0]):>5} | {loc.parts[-1]} / {loc.parts[-2]} ({[np.isfinite(ld).all(1).sum() for ld in loc_data[1:]]})') assert(all([len(l) in [len(loc_data[0]), 0] for l in loc_data])), dict(zip(keys, map(np.shape, loc_data))) - if all([l.shape[1] == 0 for l in loc_data[1:]]): + if all([l.shape[1] == 0 for l in loc_data[(1 if len(loc_data) > 1 else 0):]]): print(f'Skipping dataset {loc}: missing all features') continue @@ -398,9 +431,10 @@ def get_valid(name, loc, _wavelengths, margin=2): except Exception as e: # assert(0), e # Allow invalid datasets if there are multiple to be fetched - print(f'Error {loc}: {e}') + print(f'\nError fetching {loc}:\n\t{e}') if len(np.atleast_1d(locs)) == 1: raise e + assert(len(x_data) > 0 or len(locs) == 0), 'No datasets are valid with the given wavelengths' assert(all([x.shape[1] == x_data[0].shape[1] for x in x_data])), f'Differing number of {keys[0]} wavelengths: {[x.shape for x in x_data]}' @@ -430,9 +464,11 @@ def get_valid(name, loc, _wavelengths, margin=2): # Combine everything together l_data = np.vstack(l_data) x_data = np.vstack(x_data) - y_data = np.vstack([np.hstack(y) for y in y_data]) - assert(slices[-1] == y_data.shape[1]), [slices, y_data.shape] - assert(y_data.shape[0] == x_data.shape[0]), [x_data.shape, y_data.shape] + + if len(keys) > 0: + y_data = np.vstack([np.hstack(y) for y in y_data]) + assert(slices[-1] == y_data.shape[1]), [slices, y_data.shape] + assert(y_data.shape[0] == x_data.shape[0]), [x_data.shape, y_data.shape] slices = {k.replace('../','') : slice(slices[i], s) for i,(k,s) in enumerate(zip(keys, slices[1:]))} print(f'\tTotal prior to filtering: {len(x_data)}') @@ -502,6 +538,7 @@ def _filter_invalid(x_data, y_data, slices, allow_nan_inp=False, allow_nan_out=F both_data = [x_data, y_data] set_length = [len(fullset) for fullset in both_data] set_shape = [[len(subset) for subset in fullset] for fullset in both_data] + assert(np.all([length == len(x_data) for length in set_length])), \ f'Mismatching number of subsets: {set_length}' assert(np.all([[shape == len(fullset[0]) for shape in shapes] @@ -535,7 +572,7 @@ def _filter_invalid(x_data, y_data, slices, allow_nan_inp=False, allow_nan_out=F def get_data(args): ''' Main function for gathering datasets ''' np.random.seed(args.seed) - sensor = args.sensor.split('-')[0] + sensor = args.sensor.split('-')[0]+(('-'+args.sensor.split('-')[-1]) if '-' in args.sensor and args.sensor.split('-')[-1][0] == 'S' else '') products = args.product.split(',') bands = get_sensor_bands(args.sensor, args) @@ -572,7 +609,7 @@ def get_data(args): datasets = [d for d in datasets if d not in ['PACE']] if product == '../chl': - datasets = [d for d in datasets if d not in ['Arctic']] # Bunkei contained within sundar's set + datasets = [d for d in datasets if d not in ['Arctic']] data_folder += datasets data_keys += [product] @@ -607,7 +644,7 @@ def get_data(args): y_data = [y_data] + y_align # if -nan IS in the sensor label: do not filter samples; allow all, regardless of nan composition - if '-nan' not in args.sensor: + if '-nan' not in args.sensor: (x_data, *_), (y_data, *_), (sources, *_) = _filter_invalid(x_data, y_data, slices, other=[sources], allow_nan_out=len(data_keys) > 2) print('\nFinal counts:')