First push of the code

04_run_classification.py

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+from pathlib import Path
+
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.pipeline import make_pipeline
+from tslearn.preprocessing import TimeSeriesScalerMeanVariance
+
+from src.metadata import l_datasets_classif_bench
+from src.segment_feature import SegmentFeature
+from src.segmentation import Segmentation
+from src.symbolic_signal_distance import SymbolicSignalDistance
+from src.symbolization import Symbolization
+from src.tslearn_interface import (DistInterfaceOneD, DistInterfaceSAX, MyOneD,
+                                   MySAX)
+from src.utils_run_classification import launch_grid_search_acc_datasets
+
+
+def main(method_name, date_exp, l_datasets_to_compute):
+    """In the classification benchmark, there are 86 data sets from the UCR
+    archive which are univariate, equal-size and have at least 100 samples.
+    """
+
+    # Check if the method name is correct
+    method_names = [
+        "sax",  # our SAX implementation
+        "saxtslearn",  # SAX implementation from tslearn
+        "1dsax",
+        "astride",
+        "fastride",
+    ]
+    err_msg = f"Choose an existing method name, not {method_name}."
+    assert method_name in method_names, err_msg
+
+    # Verbose
+    print(f"\nMethod name: {method_name}")
+    print(f"Date of experiment: {date_exp}")
+    n_datasets = len(l_datasets_to_compute)
+    print(f"UCR data sets under consideration: {n_datasets}")
+
+    # Define the parameter grid of the hyper-parameters of the symbolization methods
+    param_grid_n_segments = [5, 10, 15, 20, 25]
+    param_grid_n_symbols = [4, 9, 16, 25]
+    param_grid_alphabet_size_avg = [2, 3, 4, 5]  # for 1d-SAX
+    param_grid_alphabet_size_slope = [2, 3, 4, 5]  # for 1d-SAX    
+
+    # Define the pipelines for the symbolization methods
+    pipe_symbts = make_pipeline(
+        TimeSeriesScalerMeanVariance(),
+        Segmentation(),
+        SegmentFeature(),
+        Symbolization(),
+        SymbolicSignalDistance(),
+        KNeighborsClassifier(n_neighbors=1, metric="precomputed"),
+    )
+    pipe_tslearn_sax = make_pipeline(
+        MySAX(
+            scale=True
+        ),
+        DistInterfaceSAX(),
+        KNeighborsClassifier(n_neighbors=1, metric="precomputed"),
+    )
+    pipe_tslearn_1dsax = make_pipeline(
+        MyOneD(
+            sigma_l=1.0,
+            scale=True,
+        ),
+        DistInterfaceOneD(),
+        KNeighborsClassifier(n_neighbors=1, metric="precomputed"),
+    )
+
+    # Define the parameter grid for each symbolization method
+    param_grid_sax = {
+        "segmentation__univariate_or_multivariate": ["multivariate"],
+        "segmentation__uniform_or_adaptive": ["uniform"],
+        "segmentation__mean_or_slope": [None],
+        "segmentation__n_segments": param_grid_n_segments,
+        "segmentation__pen_factor": [None],
+        "segmentfeature__features_names": [["mean"]],
+        "symbolization__n_symbols": param_grid_n_symbols,
+        "symbolization__symb_method": ["quantif"],
+        "symbolization__symb_quantif_method": ["gaussian"],
+        "symbolization__symb_cluster_method": [None],
+        "symbolization__features_scaling": [None],
+        "symbolization__reconstruct_bool": [False],
+        "symbolization__n_regime_lengths": [None],
+        "symbolization__seglen_bins_method": [None],
+        "symbolization__lookup_table_type": ["mindist"],
+        "symbolicsignaldistance__distance": ["euclidean"],
+        "symbolicsignaldistance__n_samples": [None],  # to be set
+        "symbolicsignaldistance__weighted_bool": [True],
+    }
+    param_grid_tslearn_sax = {
+        "mysax__n_segments": param_grid_n_segments,
+        "mysax__alphabet_size_avg": param_grid_n_symbols,
+    }
+    param_grid_tslearn_1dsax = {
+        "myoned__n_segments": param_grid_n_segments,
+        "myoned__alphabet_size_avg": param_grid_alphabet_size_avg,
+        "myoned__alphabet_size_slope": param_grid_alphabet_size_slope,
+    }
+    param_grid_astride = {
+        "segmentation__univariate_or_multivariate": ["multivariate"],
+        "segmentation__uniform_or_adaptive": ["adaptive"],
+        "segmentation__mean_or_slope": ["mean"],
+        "segmentation__n_segments": param_grid_n_segments,
+        "segmentation__pen_factor": [None],
+        "segmentfeature__features_names": [["mean"]],
+        "symbolization__n_symbols": param_grid_n_symbols,
+        "symbolization__symb_method": ["quantif"],
+        "symbolization__symb_quantif_method": ["quantiles"],
+        "symbolization__symb_cluster_method": [None],
+        "symbolization__features_scaling": [None],
+        "symbolization__reconstruct_bool": [True],
+        "symbolization__n_regime_lengths": ["divide_exact"],
+        "symbolization__seglen_bins_method": [None],
+        "symbolization__lookup_table_type": ["mof"],
+        "symbolicsignaldistance__distance": ["lev"],
+        "symbolicsignaldistance__n_samples": [None],
+        "symbolicsignaldistance__weighted_bool": [True],
+    }
+    param_grid_fastride = {
+        "segmentation__univariate_or_multivariate": ["multivariate"],
+        "segmentation__uniform_or_adaptive": ["uniform"],
+        "segmentation__mean_or_slope": [None],
+        "segmentation__n_segments": param_grid_n_segments,
+        "segmentation__pen_factor": [None],
+        "segmentfeature__features_names": [["mean"]],
+        "symbolization__n_symbols": param_grid_n_symbols,
+        "symbolization__symb_method": ["quantif"],
+        "symbolization__symb_quantif_method": ["quantiles"],
+        "symbolization__symb_cluster_method": [None],
+        "symbolization__features_scaling": [None],
+        "symbolization__reconstruct_bool": [False],
+        "symbolization__n_regime_lengths": [None],
+        "symbolization__seglen_bins_method": [None],
+        "symbolization__lookup_table_type": ["mof"],
+        "symbolicsignaldistance__distance": ["lev"],
+        "symbolicsignaldistance__n_samples": [None],
+        "symbolicsignaldistance__weighted_bool": [True],
+    }
+
+    # Select the pipeline and the parameter grid according to the method name
+    if method_name == "sax":
+        pipe_bench = pipe_symbts
+        param_grid_bench = param_grid_sax
+    elif method_name == "saxtslearn":
+        pipe_bench = pipe_tslearn_sax
+        param_grid_bench = param_grid_tslearn_sax
+    elif method_name == "1dsax":
+        pipe_bench = pipe_tslearn_1dsax
+        param_grid_bench = param_grid_tslearn_1dsax
+    elif method_name == "fastride":
+        pipe_bench = pipe_symbts
+        param_grid_bench = param_grid_fastride
+    elif method_name == "astride":
+        pipe_bench = pipe_symbts
+        param_grid_bench = param_grid_astride
+
+    # Launch the classification
+    _ = launch_grid_search_acc_datasets(
+        l_datasets_bench=l_datasets_to_compute,
+        pipe=pipe_bench,
+        param_grid=param_grid_bench,
+        method_name=method_name,
+        date_exp=date_exp,
+    )
+
+
+if __name__ == "__main__":
+
+    import argparse
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--method_name",
+        type=str,
+        help="Name of the method in {'sax', 'saxtslearn', '1dsax', 'astride', 'fastride'}.",
+        required=True,
+    )
+    parser.add_argument(
+        "--date_exp",
+        type=str,
+        help="Date of the launch of the experiments (for versioning).",
+        required=True,
+    )
+
+    args = parser.parse_args()
+    main(args.method_name, args.date_exp, l_datasets_classif_bench)