Add code cells markup for dataset examples to make them interactive (#39

) * Add cells markup for dataset examples to make the interactive * Unify random seed printing * Fix flake
scikit-adaptation · Dec 18, 2023 · bf11330 · bf11330
1 parent fe42db9
commit bf11330
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Showing 3 changed files with 19 additions and 4 deletions.
diff --git a/examples/datasets/plot_dataset_from_moons_distribution.py b/examples/datasets/plot_dataset_from_moons_distribution.py
@@ -6,7 +6,7 @@
 dataset generator. Each method consists of generating source data
 and shifted target data.
 """
-
+# %% Imports
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -17,6 +17,7 @@
 # ensure same distributions
 RANDOM_SEED = np.random.randint(2**10)
 
+# %% Generate and visualize single-source single-target dataset
 
 X, y, sample_domain = make_dataset_from_moons_distribution(
     pos_source=0.1,
@@ -63,6 +64,8 @@
 
 plt.show()
 
+# %% Generate and visualize multi-source multi-target dataset
+
 X, y, sample_domain = make_dataset_from_moons_distribution(
     pos_source=[0.1, 0.3, 0.5],
     pos_target=[0.4, 0.9],
@@ -78,7 +81,6 @@
 fig, (ax1, ax2) = plt.subplots(1, 2, sharex="row", sharey="row", figsize=(8, 4))
 fig.suptitle('Multi-source and Multi-target', fontsize=14)
 plt.subplots_adjust(bottom=0.15)
-# for i in sample_domain and positive
 
 for i in np.unique(domain_source):
     ax1.scatter(
@@ -114,4 +116,4 @@
 
 plt.show()
 
-print("The data was generated from (random_state=%d):" % RANDOM_SEED)
+print(f"The data was generated from (random_state={RANDOM_SEED})")
diff --git a/examples/datasets/plot_shifted_dataset.py b/examples/datasets/plot_shifted_dataset.py
@@ -13,13 +13,16 @@
        A unifying view on dataset shift in classification.
        Pattern recognition, 45(1):521-530.
 """
+# %% Imports
 
 import matplotlib.pyplot as plt
 
 from skada.datasets import make_shifted_datasets
 from skada import source_target_split
 
 
+# %% Helper function
+
 def plot_shifted_dataset(shift, random_state=42):
     """Plot source and shifted target data for a given type of shift.
 
@@ -78,6 +81,8 @@ def plot_shifted_dataset(shift, random_state=42):
     plt.show()
 
 
+# %% Visualize shifted datasets
+
 for shift in [
     "covariate_shift",
     "target_shift",

diff --git a/examples/datasets/plot_variable_frequency_dataset.py b/examples/datasets/plot_variable_frequency_dataset.py
@@ -6,6 +6,8 @@
 dataset generator. Each method consists of generating source data
 and shifted target data.
 """
+# %% Imports
+
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -16,6 +18,8 @@
 # ensure same distributions
 RANDOM_SEED = np.random.randint(2**10)
 
+# %% Generate the dataset
+
 X, y, sample_domain = make_variable_frequency_dataset(
     n_samples_source=1,
     n_samples_target=1,
@@ -31,6 +35,8 @@
 
 X_source, y_source, X_target, y_target = source_target_split(X, y, sample_domain)
 
+# %% Visualize the signal
+
 fig, ax = plt.subplots(3, 2, sharex="all", sharey="all", figsize=(8, 4))
 plt.subplots_adjust(bottom=0.15)
 fig.suptitle('Signal visualisation')
@@ -67,6 +73,8 @@
 ax[0, 0].legend()
 plt.show()
 
+# %% Visualize PSD shift
+
 fig, ax = plt.subplots(3, 2, sharex="all", sharey="all", figsize=(8, 4))
 plt.subplots_adjust(bottom=0.15)
 fig.suptitle('PSD shift')
@@ -107,4 +115,4 @@
 ax[2, 1].set_xlabel("Frequency")
 plt.show()
 
-print("The data was generated from (random_state=%d):" % RANDOM_SEED)
+print(f"The data was generated from (random_state={RANDOM_SEED})")