Reorganise files to separate notebooks and python files

.gitignore

@@ -78,8 +78,8 @@ jupyterlite_contents
 *.egg-info
 
 # generate predictions
-python_files/predictions
+content/predictions
 
 # jupyter-book build
 book/_build
-python_files/*.ipynb
+content/notebooks

book/content

@@ -1 +1 @@
-../python_files
+../content

python_files/_generate_predictions.py → content/python_files/_generate_predictions.py

@@ -1,7 +1,7 @@
 # %%
 from pathlib import Path
 
-Path.mkdir(Path.cwd() / "predictions", exist_ok=True)
+Path.mkdir(Path.cwd() / ".." / "predictions", exist_ok=True)
 
 # %%
 import numpy as np
@@ -12,7 +12,7 @@
     n_samples=100_000, class_sep=0.5, random_state=0
 )
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)
-np.save("./predictions/y_true.npy", y_test)
+np.save("../predictions/y_true.npy", y_test)
 
 # %%
 from sklearn.linear_model import LogisticRegression
@@ -24,7 +24,7 @@
 y_pred = model.predict_proba(X_test)[:, 1]
 
 # %%
-np.save("./predictions/y_prob_1.npy", y_pred)
+np.save("../predictions/y_prob_1.npy", y_pred)
 
 # %%
 from scipy.special import expit
@@ -33,7 +33,7 @@
 y_pred = expit(10 * (y_pred - 0.5))
 
 # %%
-np.save("./predictions/y_prob_2.npy", y_pred)
+np.save("../predictions/y_prob_2.npy", y_pred)
 
 
 # %%
@@ -42,12 +42,12 @@
 y_pred = (y_pred - y_pred.min()) / (y_pred.max() - y_pred.min())
 
 # %%
-np.save("./predictions/y_prob_3.npy", y_pred)
+np.save("../predictions/y_prob_3.npy", y_pred)
 
 # %%
 y_pred = model.predict_proba(X_test)[:, 1]
 y_pred = np.exp(y_pred * 5)
 y_pred = (y_pred - y_pred.min()) / (y_pred.max() - y_pred.min())
 
 # %%
-np.save("./predictions/y_prob_4.npy", y_pred)
+np.save("../predictions/y_prob_4.npy", y_pred)

python_files/build_calibration_curve.py → content/python_files/build_calibration_curve.py

@@ -20,7 +20,7 @@
 from IPython import get_ipython
 
 ipython = get_ipython()
-ipython.run_line_magic("run", "_generate_predictions.py")
+ipython.run_line_magic("run", "../python_files/_generate_predictions.py")
 
 # %% [markdown]
 #
@@ -29,8 +29,8 @@
 # %%
 import numpy as np
 
-y_true = np.load("./predictions/y_true.npy")
-y_prob = np.load("./predictions/y_prob_2.npy")
+y_true = np.load("../predictions/y_true.npy")
+y_prob = np.load("../predictions/y_prob_2.npy")
 
 # %%
 y_true

python_files/different_calibration_curves.py → content/python_files/different_calibration_curves.py

@@ -16,7 +16,7 @@
 from IPython import get_ipython
 
 ipython = get_ipython()
-ipython.run_line_magic("run", "_generate_predictions.py")
+ipython.run_line_magic("run", "../python_files/_generate_predictions.py")
 
 # %% [markdown]
 #
@@ -25,7 +25,7 @@
 # %%
 import numpy as np
 
-y_true = np.load("./predictions/y_true.npy")
+y_true = np.load("../predictions/y_true.npy")
 y_true
 
 # %% [markdown]
@@ -34,7 +34,7 @@
 # sets of predictions of probabilities estimated by different models.
 
 # %%
-y_proba = np.load("./predictions/y_prob_1.npy")
+y_proba = np.load("../predictions/y_prob_1.npy")
 y_proba
 
 # %% [markdown]
@@ -76,7 +76,7 @@
 # We now repeat the same analysis for the other sets of predictions.
 
 # %%
-y_proba = np.load("./predictions/y_prob_2.npy")
+y_proba = np.load("../predictions/y_prob_2.npy")
 disp = CalibrationDisplay.from_predictions(y_true, y_proba, **params)
 _ = disp.ax_.set(
     title="Uncalibrated model",
@@ -119,7 +119,7 @@
 # Let's use the same approach to analyze some other typical calibration curves.
 
 # %%
-y_proba = np.load("./predictions/y_prob_3.npy")
+y_proba = np.load("../predictions/y_prob_3.npy")
 disp = CalibrationDisplay.from_predictions(y_true, y_proba, **params)
 disp.ax_.axvline(0.5, color="tab:orange", label="Estimated probability = 0.5")
 disp.ax_.legend()
@@ -147,7 +147,7 @@
 # Let's check the last set of predictions.
 
 # %%
-y_proba = np.load("./predictions/y_prob_4.npy")
+y_proba = np.load("../predictions/y_prob_4.npy")
 disp = CalibrationDisplay.from_predictions(y_true, y_proba, **params)
 disp.ax_.axvline(0.5, color="tab:orange", label="Estimated probability = 0.5")
 disp.ax_.legend()

pixi.toml

@@ -7,10 +7,10 @@ channels = ["conda-forge"]
 platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"]
 
 [tasks]
-generate-predictions = { cmd = "python _generate_predictions.py", cwd = "python_files" }
-build-calibration-curve = { cmd = "ipython build_calibration_curve.py", cwd = "python_files" }
+generate-predictions = { cmd = "python _generate_predictions.py", cwd = "content/python_files" }
+build-calibration-curve = { cmd = "ipython build_calibration_curve.py", cwd = "content/python_files" }
 check-generated-predictions = { cmd = "python check_generated_predictions.py", cwd = "tests" }
-different-calibration-curves = { cmd = "ipython different_calibration_curves.py", cwd = "python_files" }
+different-calibration-curves = { cmd = "ipython different_calibration_curves.py", cwd = "content/python_files" }
 
 [dependencies]
 jupyterlab = ">=4.2.4,<5"
@@ -32,7 +32,7 @@ black = "*"
 jupyter-book = "*"
 
 [feature.doc.tasks]
-convert-to-notebooks = { cmd = "jupytext --to notebook --execute python_files/*.py" }
+convert-to-notebooks = { cmd = "jupytext --to notebook --execute ./content/python_files/*.py && mkdir -p ./content/notebooks && mv ./content/python_files/*.ipynb ./content/notebooks" }
 build-book = { cmd = "jupyter-book build book", depends-on = ["convert-to-notebooks"] }
 
 [environments]

tests/check_generated_predictions.py

@@ -1,7 +1,7 @@
 # %%
 from pathlib import Path
 
-path_to_predictions = Path(Path.cwd() / ".." / "python_files" / "predictions")
+path_to_predictions = Path(Path.cwd() / ".." / "content" / "predictions")
 assert path_to_predictions.exists()
 
 # %%