From 8da084925c0d3e87e005a162077241e996d46ffc Mon Sep 17 00:00:00 2001 From: Anna Bobasheva <33026767+AnnaBobasheva@users.noreply.github.com> Date: Tue, 15 Jul 2025 17:16:11 +0200 Subject: [PATCH 1/5] Splt the plot_neighbors example into two. --- .../neighbors/plot_neigbors_classification.py | 160 ++++++++++++++++++ .../neighbors/plot_neigbors_search.py | 104 ++++++++++++ 2 files changed, 264 insertions(+) create mode 100644 docs/examples/neighbors/plot_neigbors_classification.py create mode 100644 docs/examples/neighbors/plot_neigbors_search.py diff --git a/docs/examples/neighbors/plot_neigbors_classification.py b/docs/examples/neighbors/plot_neigbors_classification.py new file mode 100644 index 000000000..6272a29da --- /dev/null +++ b/docs/examples/neighbors/plot_neigbors_classification.py @@ -0,0 +1,160 @@ +# -*- coding: utf-8 -*- +""" +Nearest Neighbors Classification +================================ + +This example demonstrates the use of k-nearest neighbors based classifiers for time series +:class:`~tslearn.neighbors.KNeighborsTimeSeriesClassifier` and examines the impact +of different distance metrics as model parameters on the `GunPoint` time series dataset . + +We compare the predictive performance of classifiers [1] fitted with three different +metrics: Dynamic Time Warping (DTW )[2], Euclidean distance and Symbolic Aggregate +approXimation distance (SAX-MINDIST) [3] across several values of k. + +[1] `Wikipedia entry for the k-nearest neighbors algorithm +`_ + +[2] H. Sakoe and S. Chiba, "Dynamic programming algorithm optimization +for spoken word recognition". IEEE Transactions on Acoustics, Speech, and +Signal Processing, 26(1), 43-49 (1978). + +[3] J. Lin, E. Keogh, L. Wei and S. Lonardi, "Experiencing SAX: a novel +symbolic representation of time series". Data Mining and Knowledge Discovery, +15(2), 107-144 (2007). + +""" + +# sphinx_gallery_start_ignore +import warnings +warnings.filterwarnings("ignore") +# sphinx_gallery_end_ignore + +# Authors: Anna Bobasheva, Romain Tavenard +# License: BSD 3 clause + +############################################################################## +# Load the dataset +# ---------------- +# +# In this example we use the `GunPoint dataset from the UCR/UEA archive +# `_ . +# +# The dataset contains hand movement trajectories (x coordinates) +# for two different actions performed by actors: drawing a gun from a hip holster and +# pointing with a finger at a target. +# +# The dataset is scaled to the [0, 1] range to ensure that time series +# are on a comparable scale before applying distance-based classification. +from tslearn.datasets import UCR_UEA_datasets +from tslearn.preprocessing import TimeSeriesScalerMinMax + +X_train, y_train, X_test, y_test = UCR_UEA_datasets().load_dataset("GunPoint") +X_train = TimeSeriesScalerMinMax().fit_transform(X_train) +X_test = TimeSeriesScalerMinMax().fit_transform(X_test) +y_train = y_train - 1 # Convert to binary classes (0 and 1) +y_test = y_test - 1 + +############################################################################## +# Nearest neighbor classification +# -------------------------------------- +# +# We train multiple k-nearest neighbors classifiers for time series with different +# configurations: +# +# * *Distance metrics*: +# +# * *dtw* - distance metrics which can handle temporal shifts +# * *euclidean* - standard point-wise distance measurement +# * *sax* - distance metric which works on symbolic representations of time series +# +# * *k values*: number of neighbors (k) from 1 to 9 +# +# For each combination, we compute the F1 score of the test set predictions. +from sklearn.metrics import accuracy_score, f1_score +from tslearn.neighbors import KNeighborsTimeSeriesClassifier + +distance_metrics = ["dtw", "euclidean", "sax"] +k_values = [1, 3, 5, 7, 9] +results = [] + +for metric in distance_metrics: + # The SAX distance metric requires special parameters + metric_params = {'n_segments': 10, 'alphabet_size_avg': 5} if metric == "sax" else {} + + for k in k_values: + knn_clf = KNeighborsTimeSeriesClassifier(n_neighbors=k, metric=metric, + metric_params=metric_params ) + knn_clf.fit(X_train, y_train) + y_pred = knn_clf.predict(X_test) + + # Store results + results.append({ + 'metric': metric, + 'k': k, + 'f1_score': f1_score(y_test, y_pred, average='weighted') + }) + +############################################################################## +# Now we create a radar plot to visualize the F1 scores for each distance metric +# and k value combination, where: +# +# * Each axis represents a k value (1, 3, 5, 7, 9) +# * Each colored line represents a distance metric (DTW, Euclidean, SAX) +# * The distance from center shows the F1 score (higher is better) +# +# This visualization helps identifying the optimal (distance metric, k value) configuration. +import matplotlib.pyplot as plt +import numpy as np + +plt.figure(figsize=(10, 6)) +plt.subplot(111, projection='polar') + +# Number of variables +categories = [f'k={k}' for k in k_values] +N = len(categories) + +# Compute angle for each axis +angles = [n / float(N) * 2 * np.pi for n in range(N)] +angles += angles[:1] # Complete the circle + +# Colors for each metric +colors = ['#FF6B6B', '#4ECDC4', '#45B7D1'] + +for i, metric in enumerate(distance_metrics): + # Get values for this metric across all k values + values = [res['f1_score'] for k in sorted(k_values) + for res in results if res['metric'] == metric and res['k'] == k] + values += values[:1] # Complete the circle + + plt.plot(angles, values, 'o-', linewidth=2, label=metric, color=colors[i]) + plt.fill(angles, values, alpha=0.25, color=colors[i]) + +# Add best F1 score label +best_model = max(results, key=lambda x: x['f1_score']) +plt.text(1.3, 0, + f'Best score: {best_model["f1_score"]:.3f}\n@({best_model["metric"]}, k={best_model["k"]})', + ha ='right', va='bottom', weight='bold', + transform=plt.gca().transAxes, + bbox=dict(facecolor='white', alpha=0.5, edgecolor='lightgrey') ) + + +plt.xticks(angles[:-1], categories, fontsize=12) +plt.ylim(0, 1) +plt.title('F1 score Radar Chart', fontsize=16) +plt.legend(title='Distance Metric', loc='upper right', bbox_to_anchor=(1.3, 1.0)) +plt.tight_layout() +plt.show() + +############################################################################## +# Conclusion +# ---------- +# +# We observe that DTW distance generally outperforms both Euclidean and SAX metrics across most k values. +# This confirms that accounting for temporal distortion is beneficial for the `GunPoint` +# dataset where the same action may be performed at different speeds. +# +# The clear performance difference highlights why choosing an appropriate distance metric +# is crucial for time series classification tasks, particularly for datasets with temporal variations. + + + diff --git a/docs/examples/neighbors/plot_neigbors_search.py b/docs/examples/neighbors/plot_neigbors_search.py new file mode 100644 index 000000000..f51059f34 --- /dev/null +++ b/docs/examples/neighbors/plot_neigbors_search.py @@ -0,0 +1,104 @@ +# -*- coding: utf-8 -*- +""" +===================================== +Unsupervised Nearest Neighbors Search +===================================== + +This example illustrates how to perform unsupervised k-nearest neighbors [1] search using +:class:`~tslearn.neighbors.KNeighborsTimeSeries` to identify similar time series +in the `GunPoint` dataset. + +The distance between time series is calculated using Dynamic Time Warping (DTW) algorithm [2]. + +[1] `Wikipedia entry for the k-nearest neighbors algorithm +`_ + +[2] H. Sakoe and S. Chiba, "Dynamic programming algorithm optimization +for spoken word recognition". IEEE Transactions on Acoustics, Speech, and +Signal Processing, 26(1), 43-49 (1978). +""" +# sphinx_gallery_start_ignore +import warnings +warnings.filterwarnings("ignore") +# sphinx_gallery_end_ignore + +# Author: Romain Tavenard +# License: BSD 3 clause + +############################################################################## +# Load the dataset +# ---------------- +# +# In this example we use the `GunPoint dataset from the UCR/UEA archive +# `_ . +# +# The dataset contains hand movement trajectories (x coordinates) +# for two different actions performed by actors: drawing a gun from a hip holster and +# pointing with a finger at a target. +# +# The dataset is scaled to the [0, 1] range to ensure that time series +# are on a comparable scale before applying distance-based classification. +from tslearn.datasets import UCR_UEA_datasets +from tslearn.preprocessing import TimeSeriesScalerMinMax + +X_train, y_train, X_test, y_test = UCR_UEA_datasets().load_dataset("GunPoint") +X_train = TimeSeriesScalerMinMax().fit_transform(X_train) +X_test = TimeSeriesScalerMinMax().fit_transform(X_test) +y_train = y_train - 1 # Convert to binary classes (0 and 1) +y_test = y_test - 1 +labels = ["Gun", "Point"] + +############################################################################## +# Nearest neighbor search +# ------------------------ +# +# Now we fit a k-nearest neighbors model to the training data using +# :class:`~tslearn.neighbors.KNeighborsTimeSeries` and find the three nearest neighbors +# of test time series from different classes using Dynamic Time Warping (DTW) distance. +# Unlike supervised classification, this demonstrates how nearest neighbor search +# can identify similar patterns in an unsupervised manner. +from tslearn.neighbors import KNeighborsTimeSeries +knn = KNeighborsTimeSeries(n_neighbors=3, metric="dtw") +knn.fit(X_train, y_train) +dists, ind = knn.kneighbors(X_test) + +############################################################################## +# We will plot the test sample and its three nearest neighbors for two test samples: +# one where all neighbors belong to class 0 (Gun) and another where all neighbors belong +# to class 1 (Point). +import numpy as np +import matplotlib.pyplot as plt + +ind_0 = np.argmin(np.sum(y_train[ind], axis=1)) # Find test sample with class 0 neighbors only +ind_1 = np.argmax(np.sum(y_train[ind], axis=1)) # Find test sample with class 0 neighbors only + +plt.figure(figsize=(10, 6)) +for i, idx in enumerate([ind_0, ind_1]): + plt.subplot(2, 1, i + 1) + plt.plot(X_test[idx].ravel(), "k-", label="Test time series") + for j in range(3): + plt.plot(X_train[ind[idx, j]].ravel(), alpha=0.7, linestyle="dashed", + label=f"NN {j + 1} (class {labels[y_train[ind[idx, j]]]})") + + plt.ylabel("Hand position x (scaled)") + plt.legend() + +plt.suptitle("Nearest Neighbors for Test Time Series") +plt.xlabel("Time") +plt.tight_layout() +plt.show() + +############################################################################## +# Conclusion +# ---------------- +# The plots demonstrate the effectiveness of k-nearest neighbors search using DTW distance +# for time series pattern recognition. +# +# In the top subplot, we see the nearest neighbors have similar shape patterns which +# represent the "Gun" drawing movement. The test time series and its neighbors share +# a characteristic plateau pattern with fluctuations before and after. +# +# In the bottom subplot, we see the nearest neighbors from the "Point" movement class. +# Notably, these neighbors are identified as nearest despite a significant shift in time, +# which highlights a key feature of DTW distance - its ability to handle temporal distortions. + From c17d280e17a37618aeeaaad788c24626aa9abf64 Mon Sep 17 00:00:00 2001 From: Anna Bobasheva <33026767+AnnaBobasheva@users.noreply.github.com> Date: Wed, 23 Jul 2025 20:18:47 +0200 Subject: [PATCH 2/5] Fixed: using local datasets to build examples --- docs/datasets/Chinatown/Chinatown.txt | 30 ++ docs/datasets/Chinatown/Chinatown_TEST.arff | 399 ++++++++++++++++++ docs/datasets/Chinatown/Chinatown_TEST.ts | 381 +++++++++++++++++ docs/datasets/Chinatown/Chinatown_TEST.txt | 343 +++++++++++++++ docs/datasets/Chinatown/Chinatown_TRAIN.arff | 78 ++++ docs/datasets/Chinatown/Chinatown_TRAIN.ts | 58 +++ docs/datasets/Chinatown/Chinatown_TRAIN.txt | 20 + docs/datasets/Chinatown/README.md | 29 ++ .../plot_neigbors_classification_chinatown.py | 168 ++++++++ ... plot_neigbors_classification_gunpoint.py} | 24 +- .../plot_neigbors_search_chinatown.py | 114 +++++ ...ch.py => plot_neigbors_search_gunpoint.py} | 31 +- 12 files changed, 1657 insertions(+), 18 deletions(-) create mode 100644 docs/datasets/Chinatown/Chinatown.txt create mode 100644 docs/datasets/Chinatown/Chinatown_TEST.arff create mode 100644 docs/datasets/Chinatown/Chinatown_TEST.ts create mode 100644 docs/datasets/Chinatown/Chinatown_TEST.txt create mode 100644 docs/datasets/Chinatown/Chinatown_TRAIN.arff create mode 100644 docs/datasets/Chinatown/Chinatown_TRAIN.ts create mode 100644 docs/datasets/Chinatown/Chinatown_TRAIN.txt create mode 100644 docs/datasets/Chinatown/README.md create mode 100644 docs/examples/neighbors/plot_neigbors_classification_chinatown.py rename docs/examples/neighbors/{plot_neigbors_classification.py => plot_neigbors_classification_gunpoint.py} (90%) create mode 100644 docs/examples/neighbors/plot_neigbors_search_chinatown.py rename docs/examples/neighbors/{plot_neigbors_search.py => plot_neigbors_search_gunpoint.py} (82%) diff --git a/docs/datasets/Chinatown/Chinatown.txt b/docs/datasets/Chinatown/Chinatown.txt new file mode 100644 index 000000000..7ca8559d7 --- /dev/null +++ b/docs/datasets/Chinatown/Chinatown.txt @@ -0,0 +1,30 @@ +# PedestrianCountingSystem dataset + +The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future. + +We extract data of 10 locations for the whole year 2017. We make two datasets from these data. + +## MelbournePedestrian (not this file) and Chinatown + +Data are pedestrian count in Chinatown-Swanston St (North for 12 +months of the year 2017. Classes are based on whether data are from +a normal day or a weekend day. + +- Class 1: Weekend +- Class 2: Weekday + +Train size: 20 + +Test size: 343 + +Missing value: No + +Number of classses: 2 + +Time series length: 24 + +There is nothing to infer from the order of examples in the train and test set. + +Data source: City of Melbourne (see [1]). Data edited by Hoang Anh Dau. + +[1] http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4 diff --git a/docs/datasets/Chinatown/Chinatown_TEST.arff b/docs/datasets/Chinatown/Chinatown_TEST.arff new file mode 100644 index 000000000..c15e8c0aa --- /dev/null +++ b/docs/datasets/Chinatown/Chinatown_TEST.arff @@ -0,0 +1,399 @@ +%# PedestrianCountingSystem dataset +% +%The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future. +% +%We extract data of 10 locations for the whole year 2017. We make two datasets from these data. +% +%## MelbournePedestrian (not this file) and ## Chinatown +% +%Data are pedestrian count in Chinatown-Swanston St (North for 12 months of the year 2017. Classes are based on whether data are from a normal day or a weekend day. +% +%- Class 1: Weekend +%- Class 2: Weekday +% +%Train size: 20 +% +%Test size: 343 +% +%Missing value: No +% +%Number of classses: 2 +% +%Time series length: 24 +% +%There is nothing to infer from the order of examples in the train and test set. +% +%Data source: City of Melbourne (see [1]). Data edited by Hoang Anh Dau. +% +%[1] http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4 +@Relation Chinatown +@attribute att1 numeric +@attribute att2 numeric +@attribute att3 numeric +@attribute att4 numeric +@attribute att5 numeric +@attribute att6 numeric +@attribute att7 numeric +@attribute att8 numeric +@attribute att9 numeric +@attribute att10 numeric +@attribute att11 numeric +@attribute att12 numeric +@attribute att13 numeric +@attribute att14 numeric +@attribute att15 numeric +@attribute att16 numeric +@attribute att17 numeric +@attribute att18 numeric +@attribute att19 numeric +@attribute att20 numeric +@attribute att21 numeric +@attribute att22 numeric +@attribute att23 numeric +@attribute att24 numeric +@attribute target {1,2} + +@data +501.0,328.0,195.0,218.0,67.0,17.0,28.0,72.0,132.0,215.0,406.0,765.0,1207.0,1427.0,1234.0,1238.0,1107.0,1190.0,1255.0,1144.0,905.0,690.0,386.0,192.0,1 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mode 100644 index 000000000..4ffa3878c --- /dev/null +++ b/docs/datasets/Chinatown/Chinatown_TEST.ts @@ -0,0 +1,381 @@ +## PedestrianCountingSystem dataset +# +#The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future. +# +#We extract data of 10 locations for the whole year 2017. We make two datasets from these data. +# +### MelbournePedestrian (not this file) and Chinatown +# +#Data are pedestrian count in Chinatown-Swanston St (North for 12 +#months of the year 2017. Classes are based on whether data are from +#a normal day or a weekend day. +# +#- Class 1: Weekend +#- Class 2: Weekday +# +#Train size: 20 +# +#Test size: 343 +# +#Missing value: No +# +#Number of classses: 2 +# +#Time series length: 24 +# +#There is nothing to infer from the order of examples in the train and test set. +# +#Data source: City of Melbourne (see [1]). Data edited by Hoang Anh Dau. +# +#[1] http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4 +@problemName Chinatown +@timeStamps false +@missing false +@univariate true +@equalLength true +@seriesLength 24 +@classLabel true 1 2 +@data +501.0,328.0,195.0,218.0,67.0,17.0,28.0,72.0,132.0,215.0,406.0,765.0,1207.0,1427.0,1234.0,1238.0,1107.0,1190.0,1255.0,1144.0,905.0,690.0,386.0,192.0:1 +880.0,752.0,913.0,863.0,402.0,112.0,60.0,112.0,119.0,186.0,365.0,596.0,990.0,1193.0,1040.0,1063.0,1009.0,1025.0,1089.0,979.0,706.0,585.0,356.0,187.0:1 +493.0,389.0,174.0,121.0,82.0,36.0,27.0,64.0,127.0,203.0,415.0,747.0,1164.0,1414.0,1520.0,1295.0,1265.0,1430.0,1637.0,1697.0,1456.0,1319.0,1179.0,848.0:1 +616.0,323.0,162.0,166.0,68.0,26.0,34.0,68.0,123.0,263.0,815.0,1611.0,1823.0,2019.0,1763.0,1728.0,1568.0,1439.0,1431.0,1282.0,1078.0,857.0,498.0,248.0:1 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1.6560000e+03 1.4430000e+03 1.6040000e+03 1.5050000e+03 1.5530000e+03 1.3990000e+03 9.9300000e+02 5.5700000e+02 + 2.0000000e+00 1.4900000e+02 8.8000000e+01 3.9000000e+01 3.0000000e+01 2.2000000e+01 9.0000000e+00 3.0000000e+01 6.4000000e+01 2.1500000e+02 2.6700000e+02 4.8300000e+02 6.7100000e+02 1.2510000e+03 1.1740000e+03 1.0390000e+03 9.2100000e+02 8.6700000e+02 1.2470000e+03 1.2640000e+03 1.3550000e+03 1.1890000e+03 9.6100000e+02 6.9000000e+02 3.2800000e+02 diff --git a/docs/datasets/Chinatown/Chinatown_TRAIN.arff b/docs/datasets/Chinatown/Chinatown_TRAIN.arff new file mode 100644 index 000000000..1c78e757c --- /dev/null +++ b/docs/datasets/Chinatown/Chinatown_TRAIN.arff @@ -0,0 +1,78 @@ +%# PedestrianCountingSystem dataset +% +%The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future. +% +%We extract data of 10 locations for the whole year 2017. We make two datasets from these data. +% +%## MelbournePedestrian (not this problem) and +% +%## Chinatown +% +%Data are pedestrian count in Chinatown-Swanston St (North for 12 months of the year 2017. Classes are based on whether data are from a normal day or a weekend day. +% +%- Class 1: Weekend +%- Class 2: Weekday +% +%Train size: 20 +% +%Test size: 343 +% +%Missing value: No +% +%Number of classses: 2 +% +%Time series length: 24 +% +%There is nothing to infer from the order of examples in the train and test set. +% +%Data source: City of Melbourne (see [1]). Data edited by Hoang Anh Dau. +% +%[1] http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4 +@Relation Chinatown +@attribute att1 numeric +@attribute att2 numeric +@attribute att3 numeric +@attribute att4 numeric +@attribute att5 numeric +@attribute att6 numeric +@attribute att7 numeric +@attribute att8 numeric +@attribute att9 numeric +@attribute att10 numeric +@attribute att11 numeric +@attribute att12 numeric +@attribute att13 numeric +@attribute att14 numeric +@attribute att15 numeric +@attribute att16 numeric +@attribute att17 numeric +@attribute att18 numeric +@attribute att19 numeric +@attribute att20 numeric +@attribute att21 numeric +@attribute att22 numeric +@attribute att23 numeric +@attribute att24 numeric +@attribute target {1,2} + +@data +573.0,375.0,301.0,212.0,55.0,34.0,25.0,33.0,113.0,143.0,303.0,615.0,1226.0,1281.0,1221.0,1081.0,866.0,1096.0,1039.0,975.0,746.0,581.0,409.0,182.0,1 +394.0,264.0,140.0,144.0,104.0,28.0,28.0,25.0,70.0,153.0,401.0,649.0,1216.0,1399.0,1249.0,1240.0,1109.0,1137.0,1290.0,1137.0,791.0,638.0,597.0,316.0,1 +603.0,348.0,176.0,177.0,47.0,30.0,40.0,42.0,101.0,180.0,401.0,777.0,1344.0,1573.0,1408.0,1243.0,1141.0,1178.0,1256.0,1114.0,814.0,635.0,304.0,168.0,1 +428.0,309.0,199.0,117.0,82.0,43.0,24.0,64.0,152.0,183.0,408.0,797.0,1288.0,1491.0,1523.0,1460.0,1365.0,1520.0,1700.0,1797.0,1596.0,1139.0,910.0,640.0,1 +372.0,310.0,203.0,133.0,65.0,39.0,27.0,36.0,107.0,139.0,329.0,651.0,990.0,1027.0,1041.0,971.0,1104.0,844.0,1023.0,1019.0,862.0,643.0,591.0,452.0,1 +448.0,344.0,183.0,146.0,71.0,14.0,30.0,41.0,108.0,137.0,277.0,576.0,1010.0,1271.0,1264.0,1062.0,1093.0,1030.0,1069.0,1151.0,898.0,754.0,467.0,362.0,1 +621.0,322.0,221.0,150.0,65.0,40.0,42.0,84.0,148.0,190.0,341.0,685.0,1162.0,1391.0,1367.0,1279.0,1318.0,1336.0,1440.0,1479.0,1417.0,1347.0,1003.0,803.0,1 +597.0,409.0,142.0,93.0,48.0,30.0,34.0,87.0,132.0,157.0,389.0,1024.0,1648.0,1768.0,1703.0,1706.0,1520.0,1562.0,1608.0,1766.0,1533.0,1441.0,1252.0,964.0,1 +525.0,431.0,248.0,240.0,91.0,64.0,29.0,117.0,200.0,236.0,456.0,717.0,1331.0,1609.0,1563.0,1398.0,1465.0,1459.0,1631.0,1891.0,1847.0,1731.0,1375.0,1188.0,1 +587.0,382.0,165.0,192.0,130.0,44.0,21.0,35.0,73.0,132.0,299.0,639.0,1110.0,1320.0,1208.0,1069.0,1070.0,1164.0,1547.0,1575.0,1503.0,1139.0,1066.0,776.0,1 +144.0,73.0,21.0,16.0,10.0,12.0,26.0,100.0,177.0,220.0,371.0,599.0,1280.0,1346.0,1461.0,1367.0,1319.0,1205.0,1250.0,1269.0,981.0,806.0,550.0,320.0,2 +141.0,63.0,51.0,14.0,16.0,14.0,28.0,103.0,172.0,205.0,442.0,601.0,1165.0,1228.0,912.0,872.0,847.0,1227.0,1561.0,1611.0,1386.0,1007.0,810.0,680.0,2 +67.0,67.0,107.0,14.0,11.0,19.0,18.0,75.0,185.0,223.0,298.0,571.0,1044.0,1107.0,872.0,765.0,824.0,1032.0,1237.0,1084.0,759.0,543.0,377.0,168.0,2 +69.0,54.0,35.0,6.0,7.0,15.0,19.0,76.0,212.0,238.0,364.0,717.0,1223.0,1166.0,994.0,931.0,929.0,1093.0,1275.0,1161.0,1083.0,726.0,441.0,241.0,2 +142.0,104.0,55.0,33.0,51.0,13.0,36.0,77.0,185.0,222.0,437.0,739.0,1326.0,1372.0,979.0,942.0,1019.0,1250.0,1663.0,1781.0,1513.0,1188.0,1000.0,638.0,2 +256.0,171.0,104.0,67.0,51.0,26.0,25.0,41.0,170.0,192.0,334.0,801.0,1341.0,1468.0,1395.0,1221.0,1168.0,1284.0,1400.0,1321.0,1099.0,791.0,498.0,272.0,2 +130.0,68.0,52.0,23.0,10.0,16.0,28.0,128.0,226.0,271.0,402.0,652.0,1282.0,1353.0,1084.0,1124.0,920.0,1063.0,1160.0,1184.0,893.0,720.0,541.0,304.0,2 +78.0,60.0,51.0,17.0,10.0,12.0,22.0,73.0,172.0,186.0,318.0,414.0,1003.0,1153.0,981.0,846.0,872.0,1051.0,1200.0,1276.0,1004.0,841.0,525.0,315.0,2 +95.0,63.0,45.0,26.0,3.0,23.0,28.0,108.0,209.0,241.0,372.0,549.0,1206.0,1223.0,1156.0,1102.0,1083.0,1107.0,1109.0,1193.0,900.0,660.0,442.0,226.0,2 +82.0,93.0,51.0,28.0,39.0,17.0,30.0,138.0,225.0,261.0,358.0,621.0,1074.0,1176.0,914.0,858.0,830.0,928.0,954.0,871.0,730.0,506.0,262.0,150.0,2 diff --git a/docs/datasets/Chinatown/Chinatown_TRAIN.ts b/docs/datasets/Chinatown/Chinatown_TRAIN.ts new file mode 100644 index 000000000..309c42781 --- /dev/null +++ b/docs/datasets/Chinatown/Chinatown_TRAIN.ts @@ -0,0 +1,58 @@ +## PedestrianCountingSystem dataset +# +#The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future. +# +#We extract data of 10 locations for the whole year 2017. We make two datasets from these data. +# +### MelbournePedestrian (not this file) and Chinatown +# +#Data are pedestrian count in Chinatown-Swanston St (North for 12 +#months of the year 2017. Classes are based on whether data are from +#a normal day or a weekend day. +# +#- Class 1: Weekend +#- Class 2: Weekday +# +#Train size: 20 +# +#Test size: 343 +# +#Missing value: No +# +#Number of classses: 2 +# +#Time series length: 24 +# +#There is nothing to infer from the order of examples in the train and test set. +# +#Data source: City of Melbourne (see [1]). 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3.1500000e+02 + 2.0000000e+00 9.5000000e+01 6.3000000e+01 4.5000000e+01 2.6000000e+01 3.0000000e+00 2.3000000e+01 2.8000000e+01 1.0800000e+02 2.0900000e+02 2.4100000e+02 3.7200000e+02 5.4900000e+02 1.2060000e+03 1.2230000e+03 1.1560000e+03 1.1020000e+03 1.0830000e+03 1.1070000e+03 1.1090000e+03 1.1930000e+03 9.0000000e+02 6.6000000e+02 4.4200000e+02 2.2600000e+02 + 2.0000000e+00 8.2000000e+01 9.3000000e+01 5.1000000e+01 2.8000000e+01 3.9000000e+01 1.7000000e+01 3.0000000e+01 1.3800000e+02 2.2500000e+02 2.6100000e+02 3.5800000e+02 6.2100000e+02 1.0740000e+03 1.1760000e+03 9.1400000e+02 8.5800000e+02 8.3000000e+02 9.2800000e+02 9.5400000e+02 8.7100000e+02 7.3000000e+02 5.0600000e+02 2.6200000e+02 1.5000000e+02 diff --git a/docs/datasets/Chinatown/README.md b/docs/datasets/Chinatown/README.md new file mode 100644 index 000000000..c12887348 --- /dev/null +++ b/docs/datasets/Chinatown/README.md @@ -0,0 +1,29 @@ +# PedestrianCountingSystem dataset + +The City of Melbourne, Australia has developed an automated pedestrian counting system to better understand pedestrian activity within the municipality, such as how people use different city locations at different time of the day. The data analysis can facility decision making and urban planning for the future. + +We extract data of 10 locations for the whole year 2017. We make two datasets from these data. + +## MelbournePedestrian (not this file) and +## Chinatown + +Data are pedestrian count in Chinatown-Swanston St (North for 12 months of the year 2017. Classes are based on whether data are from a normal day or a weekend day. + +- Class 1: Weekend +- Class 2: Weekday + +Train size: 20 + +Test size: 343 + +Missing value: No + +Number of classses: 2 + +Time series length: 24 + +There is nothing to infer from the order of examples in the train and test set. + +Data source: City of Melbourne (see [1]). Data edited by Hoang Anh Dau. + +[1] http://www.pedestrian.melbourne.vic.gov.au/#date=11-06-2018&time=4 diff --git a/docs/examples/neighbors/plot_neigbors_classification_chinatown.py b/docs/examples/neighbors/plot_neigbors_classification_chinatown.py new file mode 100644 index 000000000..5904d3eff --- /dev/null +++ b/docs/examples/neighbors/plot_neigbors_classification_chinatown.py @@ -0,0 +1,168 @@ +# -*- coding: utf-8 -*- +""" +Nearest Neighbors Classification (Chinatown dataset) +==================================================== + +This example demonstrates the use of k-nearest neighbors based classifiers for time series +:class:`~tslearn.neighbors.KNeighborsTimeSeriesClassifier` and examines the impact +of different distance metrics as model parameters on the `Chinatown` time series dataset . + +We compare the predictive performance of classifiers [1] fitted with three different +metrics: Dynamic Time Warping (DTW )[2], Euclidean distance and Symbolic Aggregate +approXimation distance (SAX-MINDIST) [3] across several values of k. + +[1] `Wikipedia entry for the k-nearest neighbors algorithm +`_ + +[2] H. Sakoe and S. Chiba, "Dynamic programming algorithm optimization +for spoken word recognition". IEEE Transactions on Acoustics, Speech, and +Signal Processing, 26(1), 43-49 (1978). + +[3] J. Lin, E. Keogh, L. Wei and S. Lonardi, "Experiencing SAX: a novel +symbolic representation of time series". Data Mining and Knowledge Discovery, +15(2), 107-144 (2007). + +""" + +# sphinx_gallery_start_ignore +import warnings +warnings.filterwarnings("ignore") +# sphinx_gallery_end_ignore + +# Authors: Romain Tavenard, Anna Bobasheva +# License: BSD 3 clause + +############################################################################## +# Load the dataset +# ---------------- +# +# In this example we use the `Chinatown dataset from the UCR/UEA archive +# `_ . +# +# The dataset consists of pedestrian counts recorded at the Chinatown location in +# Melbourne, Australia. Each time series represents the number of pedestrians +# detected during a day, weekday or weekend. +# +# The dataset is scaled to the [0, 1] range to ensure that time series +# are on a comparable scale before applying distance-based classification. +from tslearn.datasets import UCR_UEA_datasets +from tslearn.preprocessing import TimeSeriesScalerMinMax + +loader = UCR_UEA_datasets() +# sphinx_gallery_start_ignore +if "__file__" not in locals(): + # runs by sphinx-gallery + import os + loader._data_dir = os.path.join( + os.path.dirname(os.path.realpath(os.getcwd())), '..', "datasets" + ) +# sphinx_gallery_end_ignore +X_train, y_train, X_test, y_test = loader.load_dataset("Chinatown") + +scaler = TimeSeriesScalerMinMax() +X_train, X_test = scaler.fit_transform(X_train), scaler.fit_transform(X_test) + +############################################################################## +# Nearest neighbor classification +# -------------------------------------- +# +# We train multiple k-nearest neighbors classifiers for time series with different +# configurations: +# +# * *Distance metrics*: +# +# * *dtw* - distance metrics which can handle temporal shifts +# * *euclidean* - standard point-wise distance measurement +# * *sax* - distance metric which works on symbolic representations of time series +# +# * *k values*: number of neighbors (k) from 1 to 9 +# +# For each combination, we compute the F1 score of the test set predictions. +from sklearn.metrics import accuracy_score, f1_score +from tslearn.neighbors import KNeighborsTimeSeriesClassifier + +distance_metrics = ["dtw", "euclidean", "sax"] +k_values = [1, 3, 5, 7, 9] +results = [] + +for metric in distance_metrics: + # The SAX distance metric requires special parameters + metric_params = {'n_segments': 10, 'alphabet_size_avg': 5} if metric == "sax" else {} + + for k in k_values: + knn_clf = KNeighborsTimeSeriesClassifier(n_neighbors=k, metric=metric, + metric_params=metric_params ) + knn_clf.fit(X_train, y_train) + y_pred = knn_clf.predict(X_test) + + # Store results + results.append({ + 'metric': metric, + 'k': k, + 'f1_score': f1_score(y_test, y_pred, average='weighted') + }) + +############################################################################## +# Now we create a radar plot to visualize the F1 scores for each distance metric +# and k value combination, where: +# +# * Each axis represents a k value (1, 3, 5, 7, 9) +# * Each colored line represents a distance metric (DTW, Euclidean, SAX) +# * The distance from center shows the F1 score (higher is better) +# +# This visualization helps identifying the optimal (distance metric, k value) configuration. +import matplotlib.pyplot as plt +import numpy as np + +plt.figure(figsize=(10, 6)) +plt.subplot(111, projection='polar') + +# Number of variables +categories = [f'k={k}' for k in k_values] +N = len(categories) + +# Compute angle for each axis +angles = [n / float(N) * 2 * np.pi for n in range(N)] +angles += angles[:1] # Complete the circle + +# Colors for each metric +colors = ['#FF6B6B', '#4ECDC4', '#45B7D1'] + +for i, metric in enumerate(distance_metrics): + # Get values for this metric across all k values + values = [res['f1_score'] for k in sorted(k_values) + for res in results if res['metric'] == metric and res['k'] == k] + values += values[:1] # Complete the circle + + plt.plot(angles, values, 'o-', linewidth=2, label=metric, color=colors[i]) + plt.fill(angles, values, alpha=0.25, color=colors[i]) + +# Add best F1 score label +best_model = max(results, key=lambda x: x['f1_score']) +plt.text(1.3, 0, + f'Best score: {best_model["f1_score"]:.3f}\n@({best_model["metric"]}, k={best_model["k"]})', + ha ='right', va='bottom', weight='bold', + transform=plt.gca().transAxes, + bbox=dict(facecolor='white', alpha=0.5, edgecolor='lightgrey') ) + + +plt.xticks(angles[:-1], categories, fontsize=12) +plt.ylim(0, 1) +plt.title('F1 score Radar Chart', fontsize=16) +plt.legend(title='Distance Metric', loc='upper right', bbox_to_anchor=(1.3, 1.0)) +plt.tight_layout() +plt.show() + +############################################################################## +# Conclusion +# ---------- +# +# We observe that DTW distance generally outperforms both Euclidean and SAX metrics across most k values. +# This confirms that accounting for temporal distortion is beneficial for the `Chinatown` +# dataset where the same action may be performed at different speeds. +# +# The clear performance difference highlights why choosing an appropriate distance metric +# is crucial for time series classification tasks, particularly for datasets with temporal variations. + + + diff --git a/docs/examples/neighbors/plot_neigbors_classification.py b/docs/examples/neighbors/plot_neigbors_classification_gunpoint.py similarity index 90% rename from docs/examples/neighbors/plot_neigbors_classification.py rename to docs/examples/neighbors/plot_neigbors_classification_gunpoint.py index 6272a29da..66da067d8 100644 --- a/docs/examples/neighbors/plot_neigbors_classification.py +++ b/docs/examples/neighbors/plot_neigbors_classification_gunpoint.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- """ -Nearest Neighbors Classification -================================ +Nearest Neighbors Classification (GunPoint Dataset) +=================================================== This example demonstrates the use of k-nearest neighbors based classifiers for time series :class:`~tslearn.neighbors.KNeighborsTimeSeriesClassifier` and examines the impact @@ -29,7 +29,7 @@ warnings.filterwarnings("ignore") # sphinx_gallery_end_ignore -# Authors: Anna Bobasheva, Romain Tavenard +# Authors: Romain Tavenard, Anna Bobasheva # License: BSD 3 clause ############################################################################## @@ -48,11 +48,19 @@ from tslearn.datasets import UCR_UEA_datasets from tslearn.preprocessing import TimeSeriesScalerMinMax -X_train, y_train, X_test, y_test = UCR_UEA_datasets().load_dataset("GunPoint") -X_train = TimeSeriesScalerMinMax().fit_transform(X_train) -X_test = TimeSeriesScalerMinMax().fit_transform(X_test) -y_train = y_train - 1 # Convert to binary classes (0 and 1) -y_test = y_test - 1 +loader = UCR_UEA_datasets() +# sphinx_gallery_start_ignore +if "__file__" not in locals(): + # runs by sphinx-gallery + import os + loader._data_dir = os.path.join( + os.path.dirname(os.path.realpath(os.getcwd())), '..', "datasets" + ) +# sphinx_gallery_end_ignore +X_train, y_train, X_test, y_test = loader.load_dataset("GunPoint") + +scaler = TimeSeriesScalerMinMax() +X_train, X_test = scaler.fit_transform(X_train), scaler.fit_transform(X_test) ############################################################################## # Nearest neighbor classification diff --git a/docs/examples/neighbors/plot_neigbors_search_chinatown.py b/docs/examples/neighbors/plot_neigbors_search_chinatown.py new file mode 100644 index 000000000..f945a0715 --- /dev/null +++ b/docs/examples/neighbors/plot_neigbors_search_chinatown.py @@ -0,0 +1,114 @@ +# -*- coding: utf-8 -*- +""" +========================================================== +Unsupervised Nearest Neighbors Search (Chinatown dataset) +========================================================== + +This example illustrates how to perform unsupervised k-nearest neighbors [1] search using +:class:`~tslearn.neighbors.KNeighborsTimeSeries` to identify similar time series +in the `Chinatown` dataset. + +The distance between time series is calculated using Dynamic Time Warping (DTW) algorithm [2]. + +[1] `Wikipedia entry for the k-nearest neighbors algorithm +`_ + +[2] H. Sakoe and S. Chiba, "Dynamic programming algorithm optimization +for spoken word recognition". IEEE Transactions on Acoustics, Speech, and +Signal Processing, 26(1), 43-49 (1978). +""" +# sphinx_gallery_start_ignore +import warnings +warnings.filterwarnings("ignore") +# sphinx_gallery_end_ignore + +# Authors: Romain Tavenard, Anna Bobasheva +# License: BSD 3 clause + +############################################################################## +# Load the dataset +# ---------------- +# +# In this example we use the `Chinatown dataset from the UCR/UEA archive +# `_ . +# +# The dataset consists of pedestrian counts recorded at the Chinatown location in Melbourne, Australia. +# Each time series represents the number of pedestrians detected during a day, weekday or weekend. +# +# The dataset is scaled to the [0, 1] range to ensure that time series +# are on a comparable scale before applying distance-based classification. +from tslearn.datasets import UCR_UEA_datasets +from tslearn.preprocessing import TimeSeriesScalerMinMax + +loader = UCR_UEA_datasets() +# sphinx_gallery_start_ignore +if "__file__" not in locals(): + # runs by sphinx-gallery + import os + loader._data_dir = os.path.join( + os.path.dirname(os.path.realpath(os.getcwd())), '..', "datasets" + ) +# sphinx_gallery_end_ignore +X_train, y_train, X_test, y_test = loader.load_dataset("Chinatown") + +scaler = TimeSeriesScalerMinMax() +X_train, X_test = scaler.fit_transform(X_train), scaler.fit_transform(X_test) + +# Convert class labels to zero-based indexing (0 and 1) for convenience +y_train, y_test = y_train - 1, y_test - 1 +labels = ["Weekend", "Weekday"] + +############################################################################## +# Nearest neighbor search +# ----------------------- +# +# Now we fit a k-nearest neighbors model to the training data using +# :class:`~tslearn.neighbors.KNeighborsTimeSeries` and find the three nearest neighbors +# of test time series from different classes using Dynamic Time Warping (DTW) distance. +# Unlike supervised classification, this demonstrates how nearest neighbor search +# can identify similar patterns in an unsupervised manner. +from tslearn.neighbors import KNeighborsTimeSeries +knn = KNeighborsTimeSeries(n_neighbors=3, metric="dtw") +knn.fit(X_train, y_train) +dists, ind = knn.kneighbors(X_test) + +############################################################################## +# We will plot the test sample and its three nearest neighbors for two test samples: +# one where all neighbors belong to class 0 (Weekend) and another where all neighbors belong +# to class 1 (Weekday). +import numpy as np +import matplotlib.pyplot as plt + +ind_0 = np.argmin(np.sum(y_train[ind], axis=1)) # Find test sample with class 0 neighbors only +ind_1 = np.argmax(np.sum(y_train[ind], axis=1)) # Find test sample with class 1 neighbors only + +plt.figure(figsize=(10, 6)) +for i, idx in enumerate([ind_0, ind_1]): + plt.subplot(2, 1, i + 1) + plt.plot(X_test[idx].ravel(), "k-", label="Test time series") + for j in range(3): + plt.plot(X_train[ind[idx, j]].ravel(), alpha=0.7, linestyle="dashed", + label=f"NN {j + 1} (class {labels[y_train[ind[idx, j]]]})") + + plt.ylabel(f"Pedestrian count (scaled)") + plt.legend() + +plt.suptitle("Nearest Neighbors for Test Time Series\nMelbourne's Chinatown Pedestrian Count") +plt.xlabel("Time") +plt.tight_layout() +plt.show() + +############################################################################## +# Conclusion +# ---------------- +# The plots demonstrate the effectiveness of k-nearest neighbors search using DTW distance +# for time series pattern recognition. +# +# In the top subplot, we see the nearest neighbors have similar shape patterns which +# represent the "Weekend" pedestrian movement pattern. The test time series and its neighbors share +# a characteristic higher night activity. +# +# In the bottom subplot, we see the nearest neighbors from the "Weekday" movement class. +# Notably, these neighbors are identified as nearest despite some shift in peak times, +# which highlights a key feature of DTW distance - its ability to handle temporal distortions. + diff --git a/docs/examples/neighbors/plot_neigbors_search.py b/docs/examples/neighbors/plot_neigbors_search_gunpoint.py similarity index 82% rename from docs/examples/neighbors/plot_neigbors_search.py rename to docs/examples/neighbors/plot_neigbors_search_gunpoint.py index f51059f34..57c068e16 100644 --- a/docs/examples/neighbors/plot_neigbors_search.py +++ b/docs/examples/neighbors/plot_neigbors_search_gunpoint.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -===================================== -Unsupervised Nearest Neighbors Search -===================================== +======================================================== +Unsupervised Nearest Neighbors Search (GunPoint dataset) +======================================================== This example illustrates how to perform unsupervised k-nearest neighbors [1] search using :class:`~tslearn.neighbors.KNeighborsTimeSeries` to identify similar time series @@ -22,7 +22,7 @@ warnings.filterwarnings("ignore") # sphinx_gallery_end_ignore -# Author: Romain Tavenard +# Authors: Romain Tavenard, Anna Bobasheva # License: BSD 3 clause ############################################################################## @@ -41,11 +41,22 @@ from tslearn.datasets import UCR_UEA_datasets from tslearn.preprocessing import TimeSeriesScalerMinMax -X_train, y_train, X_test, y_test = UCR_UEA_datasets().load_dataset("GunPoint") -X_train = TimeSeriesScalerMinMax().fit_transform(X_train) -X_test = TimeSeriesScalerMinMax().fit_transform(X_test) -y_train = y_train - 1 # Convert to binary classes (0 and 1) -y_test = y_test - 1 +loader = UCR_UEA_datasets() +# sphinx_gallery_start_ignore +if "__file__" not in locals(): + # runs by sphinx-gallery + import os + loader._data_dir = os.path.join( + os.path.dirname(os.path.realpath(os.getcwd())), '..', "datasets" + ) +# sphinx_gallery_end_ignore +X_train, y_train, X_test, y_test = loader.load_dataset("GunPoint") + +scaler = TimeSeriesScalerMinMax() +X_train, X_test = scaler.fit_transform(X_train), scaler.fit_transform(X_test) + +# Convert class labels to zero-based indexing (0 and 1) for convenience +y_train, y_test = y_train - 1, y_test - 1 labels = ["Gun", "Point"] ############################################################################## @@ -70,7 +81,7 @@ import matplotlib.pyplot as plt ind_0 = np.argmin(np.sum(y_train[ind], axis=1)) # Find test sample with class 0 neighbors only -ind_1 = np.argmax(np.sum(y_train[ind], axis=1)) # Find test sample with class 0 neighbors only +ind_1 = np.argmax(np.sum(y_train[ind], axis=1)) # Find test sample with class 1 neighbors only plt.figure(figsize=(10, 6)) for i, idx in enumerate([ind_0, ind_1]): From f112ce59d3a5c4027fe0d72b93b87fb987d06d82 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 11 Aug 2025 16:07:20 +0200 Subject: [PATCH 3/5] try with Trace --- .../plot_neigbors_classification_Trace.py | 166 ++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100644 docs/examples/neighbors/plot_neigbors_classification_Trace.py diff --git a/docs/examples/neighbors/plot_neigbors_classification_Trace.py b/docs/examples/neighbors/plot_neigbors_classification_Trace.py new file mode 100644 index 000000000..4ced45cd9 --- /dev/null +++ b/docs/examples/neighbors/plot_neigbors_classification_Trace.py @@ -0,0 +1,166 @@ +# -*- coding: utf-8 -*- +""" +Nearest Neighbors Classification (Trace Dataset) +=================================================== + +This example demonstrates the use of k-nearest neighbors based classifiers for time series +:class:`~tslearn.neighbors.KNeighborsTimeSeriesClassifier` and examines the impact +of different distance metrics as model parameters on the `Trace` time series dataset . + +We compare the predictive performance of classifiers [1] fitted with three different +metrics: Dynamic Time Warping (DTW )[2], Euclidean distance and Symbolic Aggregate +approXimation distance (SAX-MINDIST) [3] across several values of k. + +[1] `Wikipedia entry for the k-nearest neighbors algorithm +`_ + +[2] H. Sakoe and S. Chiba, "Dynamic programming algorithm optimization +for spoken word recognition". IEEE Transactions on Acoustics, Speech, and +Signal Processing, 26(1), 43-49 (1978). + +[3] J. Lin, E. Keogh, L. Wei and S. Lonardi, "Experiencing SAX: a novel +symbolic representation of time series". Data Mining and Knowledge Discovery, +15(2), 107-144 (2007). + +""" + +# sphinx_gallery_start_ignore +import warnings +warnings.filterwarnings("ignore") +# sphinx_gallery_end_ignore + +# Authors: Romain Tavenard, Anna Bobasheva +# License: BSD 3 clause + +############################################################################## +# Load the dataset +# ---------------- +# +# In this example we use the `Trace dataset from the UCR/UEA archive +# `_ . +# +# TODO explain the dataset +# +# The dataset is scaled to the [0, 1] range to ensure that time series +# are on a comparable scale before applying distance-based classification. +from tslearn.datasets import UCR_UEA_datasets +from tslearn.preprocessing import TimeSeriesScalerMinMax + +loader = UCR_UEA_datasets() +# sphinx_gallery_start_ignore +if "__file__" not in locals(): + # runs by sphinx-gallery + import os + loader._data_dir = os.path.join( + os.path.dirname(os.path.realpath(os.getcwd())), '..', "datasets" + ) +# sphinx_gallery_end_ignore +X_train, y_train, X_test, y_test = loader.load_dataset("Trace") + +scaler = TimeSeriesScalerMinMax() +X_train, X_test = scaler.fit_transform(X_train), scaler.fit_transform(X_test) + +############################################################################## +# Nearest neighbor classification +# -------------------------------------- +# +# We train multiple k-nearest neighbors classifiers for time series with different +# configurations: +# +# * *Distance metrics*: +# +# * *dtw* - distance metrics which can handle temporal shifts +# * *euclidean* - standard point-wise distance measurement +# * *sax* - distance metric which works on symbolic representations of time series +# +# * *k values*: number of neighbors (k) from 1 to 9 +# +# For each combination, we compute the F1 score of the test set predictions. +from sklearn.metrics import accuracy_score, f1_score +from tslearn.neighbors import KNeighborsTimeSeriesClassifier + +distance_metrics = ["dtw", "euclidean", "sax"] +k_values = [1, 3, 5, 7, 9] +results = [] + +for metric in distance_metrics: + # The SAX distance metric requires special parameters + metric_params = {'n_segments': 10, 'alphabet_size_avg': 5} if metric == "sax" else {} + + for k in k_values: + knn_clf = KNeighborsTimeSeriesClassifier(n_neighbors=k, metric=metric, + metric_params=metric_params ) + knn_clf.fit(X_train, y_train) + y_pred = knn_clf.predict(X_test) + + # Store results + results.append({ + 'metric': metric, + 'k': k, + 'f1_score': f1_score(y_test, y_pred, average='weighted') + }) + +############################################################################## +# Now we create a radar plot to visualize the F1 scores for each distance metric +# and k value combination, where: +# +# * Each axis represents a k value (1, 3, 5, 7, 9) +# * Each colored line represents a distance metric (DTW, Euclidean, SAX) +# * The distance from center shows the F1 score (higher is better) +# +# This visualization helps identifying the optimal (distance metric, k value) configuration. +import matplotlib.pyplot as plt +import numpy as np + +plt.figure(figsize=(10, 6)) +plt.subplot(111, projection='polar') + +# Number of variables +categories = [f'k={k}' for k in k_values] +N = len(categories) + +# Compute angle for each axis +angles = [n / float(N) * 2 * np.pi for n in range(N)] +angles += angles[:1] # Complete the circle + +# Colors for each metric +colors = ['#FF6B6B', '#4ECDC4', '#45B7D1'] + +for i, metric in enumerate(distance_metrics): + # Get values for this metric across all k values + values = [res['f1_score'] for k in sorted(k_values) + for res in results if res['metric'] == metric and res['k'] == k] + values += values[:1] # Complete the circle + + plt.plot(angles, values, 'o-', linewidth=2, label=metric, color=colors[i]) + plt.fill(angles, values, alpha=0.25, color=colors[i]) + +# Add best F1 score label +best_model = max(results, key=lambda x: x['f1_score']) +plt.text(1.3, 0, + f'Best score: {best_model["f1_score"]:.3f}\n@({best_model["metric"]}, k={best_model["k"]})', + ha ='right', va='bottom', weight='bold', + transform=plt.gca().transAxes, + bbox=dict(facecolor='white', alpha=0.5, edgecolor='lightgrey') ) + + +plt.xticks(angles[:-1], categories, fontsize=12) +plt.ylim(0, 1) +plt.title('F1 score Radar Chart', fontsize=16) +plt.legend(title='Distance Metric', loc='upper right', bbox_to_anchor=(1.3, 1.0)) +plt.tight_layout() +plt.show() + +############################################################################## +# Conclusion +# ---------- +# +# We observe that DTW distance generally outperforms both Euclidean and SAX metrics across most k values. +# This confirms that accounting for temporal distortion is beneficial for the `Trace` +# dataset where TODO explain why the DTW metric is better than the others. +# +# The clear performance difference highlights why choosing an appropriate distance metric +# is crucial for time series classification tasks, particularly for datasets with temporal variations. + + + From 185ed87269e710f83ec3b2f34da8c31538ab5cab Mon Sep 17 00:00:00 2001 From: charavelg Date: Tue, 19 Aug 2025 18:00:57 +0200 Subject: [PATCH 4/5] Update sonarqube.yaml --- .github/workflows/sonarqube.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/sonarqube.yaml b/.github/workflows/sonarqube.yaml index 612ab336c..9f983a287 100644 --- a/.github/workflows/sonarqube.yaml +++ b/.github/workflows/sonarqube.yaml @@ -11,6 +11,7 @@ jobs: build: name: Build and analyze runs-on: ubuntu-latest + environment: test steps: - uses: actions/checkout@v4 From 19faa201774da438f464ce4efab9f67ee9cd7d59 Mon Sep 17 00:00:00 2001 From: charavelg Date: Wed, 20 Aug 2025 09:04:44 +0200 Subject: [PATCH 5/5] Update sonarqube.yaml --- .github/workflows/sonarqube.yaml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/sonarqube.yaml b/.github/workflows/sonarqube.yaml index 9f983a287..612ab336c 100644 --- a/.github/workflows/sonarqube.yaml +++ b/.github/workflows/sonarqube.yaml @@ -11,7 +11,6 @@ jobs: build: name: Build and analyze runs-on: ubuntu-latest - environment: test steps: - uses: actions/checkout@v4