put()) some useful items in it.
Let us load the project and add an integer to it for example:
-from skore import load
+from skore import load
-my_project = load("my_project.skore")
-my_project.put("my_int", 3)
+my_project = load("my_project.skore")
+my_project.put("my_int", 3)
@@ -1035,9 +1035,9 @@ Example of machine learning usage: hyperparameter sweep
Finally, we store some relevant information to our skore project, so that we
can visualize them later in the skore UI for example:
-my_project.put("my_gs_cv", gs_cv)
-my_project.put("my_df", df)
-my_project.put("my_fig", fig)
+my_project.put("my_gs_cv", gs_cv)
+my_project.put("my_df", df)
+my_project.put("my_fig", fig)
@@ -1053,11 +1053,11 @@ Cross-validation with skoreput())
a cross_validation item with a plotly chart in your project.
-from skore import cross_validate
+from skore import cross_validate
-cv_results = cross_validate(Ridge(), X, y, cv=5, project=my_project)
+cv_results = cross_validate(Ridge(), X, y, cv=5, project=my_project)
-fig_plotly = my_project.get_item("cross_validation").plot
+fig_plotly = my_project.get_item("cross_validation").plot
@@ -1090,7 +1090,7 @@ Manipulating the skore UI
-
Total running time of the script: (0 minutes 6.514 seconds)
+Total running time of the script: (0 minutes 7.030 seconds)
@@ -492,46 +492,46 @@ Creating and loading a skore projectCompletedProcess(args=['python3', '-m', 'skore', 'create', 'my_project_ui'], returncode=0)
-from skore import load
+from skore import load
-my_project_ui = load("my_project_ui.skore")
+my_project_ui = load("my_project_ui.skore")
Storing integers#
Now, let us store our first object using put(), for example an integer:
-my_project_ui.put("my_int", 3)
+my_project_ui.put("my_int", 3)
Here, the name of the object is my_int and the integer value is 3.
You can read it from the project by using get():
-my_project_ui.get("my_int")
+my_project_ui.get("my_int")
3
Careful; like in a traditional Python dictionary, the put method will overwrite past data if you use a key which already exists!
-my_project_ui.put("my_int", 30_000)
+my_project_ui.put("my_int", 30_000)
Let us check the updated value:
-my_project_ui.get("my_int")
+my_project_ui.get("my_int")
30000
By using the delete_item() method, you can also delete an object so that your skore UI does not become cluttered:
-my_project_ui.put("my_int_2", 10)
+my_project_ui.put("my_int_2", 10)
-my_project_ui.delete_item("my_int_2")
+my_project_ui.delete_item("my_int_2")
You can display all the keys in your project:
-my_project_ui.list_item_keys()
+
['my_int']
@@ -541,17 +541,17 @@ Storing integers
Storing strings and texts#
We just stored a integer, now let us store some text using strings!
-my_project_ui.put("my_string", "Hello world!")
+my_project_ui.put("my_string", "Hello world!")
-my_project_ui.get("my_string")
+my_project_ui.get("my_string")
'Hello world!'
get() infers the type of the inserted object by default. For example, strings are assumed to be in Markdown format. Hence, you can customize the display of your text:
-my_project_ui.put(
+my_project_ui.put(
"my_string_2",
(
"""Hello world!, **bold**, *italic*, `code`
@@ -566,7 +566,7 @@ Storing strings and textsmy_project_ui.put_item(
+my_project_ui.put_item(
"my_string_3",
MediaItem.factory(
"<p><h1>Title</h1> <b>bold</b>, <i>italic</i>, etc.</p>",
@@ -580,7 +580,7 @@ Storing strings and textsput_item() instead of put().
Note that the media type is only used for the UI, and not in this notebook at hand:
-my_project_ui.get("my_string_3")
+my_project_ui.get("my_string_3")
b'<p><h1>Title</h1> <b>bold</b>, <i>italic</i>, etc.</p>'
@@ -589,7 +589,7 @@ Storing strings and textsx = 2
y = [1, 2, 3, 4]
-my_project_ui.put(
+my_project_ui.put(
"my_string_4", f"The value of `x` is {x} and the value of `y` is {y}."
)
@@ -599,7 +599,7 @@ Storing strings and texts#
Python list:
my_list = [1, 2, 3, 4]
-my_project_ui.put("my_list", my_list)
+my_project_ui.put("my_list", my_list)
my_list
@@ -611,7 +611,7 @@ Storing many kinds of data"company": "probabl",
"year": 2023,
}
-my_project_ui.put("my_dict", my_dict)
+my_project_ui.put("my_dict", my_dict)
my_dict
@@ -620,18 +620,18 @@ Storing many kinds of datamy_arr = np.random.randn(3, 3)
-my_project_ui.put("my_arr", my_arr)
+my_project_ui.put("my_arr", my_arr)
my_arr
-array([[ 1.07940211, -0.17079627, 0.32707525],
- [ 0.72808672, -0.3453953 , 0.58475624],
- [-1.72280206, -0.3701965 , 0.31195055]])
+array([[ 1.63853041, -1.28706686, -0.37875963],
+ [-1.05853815, 2.3366351 , -1.48016548],
+ [ 0.76746085, -0.03573865, -0.10589976]])
Pandas data frame:
my_df = pd.DataFrame(np.random.randn(10, 5))
-my_project_ui.put("my_df", my_df)
+my_project_ui.put("my_df", my_df)
my_df.head()
@@ -664,43 +664,43 @@ Storing many kinds of dataax.legend()
plt.show()
-my_project_ui.put("my_figure", fig)
+my_project_ui.put("my_figure", fig)
Altair chart:
@@ -741,7 +741,7 @@ Storing data visualizations.properties(title="My title")
)
-my_project_ui.put("my_altair_chart", my_altair_chart)
+my_project_ui.put("my_altair_chart", my_altair_chart)
@@ -764,7 +764,7 @@ Storing data visualizationssize=df.petal_length
)
-my_project_ui.put("my_plotly_fig", fig)
+my_project_ui.put("my_plotly_fig", fig)
Animated Plotly figure:
@@ -784,7 +784,7 @@ Storing data visualizationsrange_y=[25, 90],
)
-my_project_ui.put("my_anim_plotly_fig", my_anim_plotly_fig)
+my_project_ui.put("my_anim_plotly_fig", my_anim_plotly_fig)
PIL image:
@@ -792,7 +792,7 @@ Storing data visualizationswith io.BytesIO() as output:
my_pil_image.save(output, format="png")
-my_project_ui.put("my_pil_image", my_pil_image)
+my_project_ui.put("my_pil_image", my_pil_image)
@@ -801,7 +801,7 @@ Storing scikit-learn models and pipelinesProbabl, the spin-off of scikit-learn, skore treats scikit-learn models and pipelines as first-class citizens.
First of all, you can store a scikit-learn model:
@@ -1217,7 +1217,7 @@ Storing scikit-learn models and pipelinesmy_pipeline = Pipeline(
[("standard_scaler", StandardScaler()), ("lasso", Lasso(alpha=2))]
)
-my_project_ui.put("my_pipeline", my_pipeline)
+my_project_ui.put("my_pipeline", my_pipeline)
my_pipeline
@@ -1637,7 +1637,7 @@ Storing scikit-learn models and pipelinesy = diabetes.target[:150]
my_pipeline.fit(X, y)
-my_project_ui.put("my_fitted_pipeline", my_pipeline)
+my_project_ui.put("my_fitted_pipeline", my_pipeline)
my_pipeline
@@ -2051,7 +2051,7 @@ Storing scikit-learn models and pipelinesStandardScaler()
Lasso(alpha=2)
-
Total running time of the script: (0 minutes 1.417 seconds)
+
Total running time of the script: (0 minutes 1.439 seconds)
@@ -487,7 +487,7 @@ Creating and loading the skore projectCompletedProcess(args=['python3', '-m', 'skore', 'create', 'my_project_cv'], returncode=0)
-my_project_gs = load("my_project_cv.skore")
+my_project_gs = load("my_project_cv.skore")
@@ -556,15 +556,15 @@ Cross-validation in skore
Classification task#
Let us continue with the same use case.
-cv_results = skore.cross_validate(clf, X, y, cv=5, project=my_project_gs)
+cv_results = skore.cross_validate(clf, X, y, cv=5, project=my_project_gs)
-fig_plotly_clf = my_project_gs.get_item("cross_validation").plot
+fig_plotly_clf = my_project_gs.get_item("cross_validation").plot
fig_plotly_clf
Skore’s cross_validate advantages are the following:
@@ -604,18 +604,18 @@ Regression tasky = diabetes.target[:150]
lasso = linear_model.Lasso()
-cv_results = skore.cross_validate(lasso, X, y, cv=5, project=my_project_gs)
+cv_results = skore.cross_validate(lasso, X, y, cv=5, project=my_project_gs)
-fig_plotly_reg = my_project_gs.get_item("cross_validation").plot
+fig_plotly_reg = my_project_gs.get_item("cross_validation").plot
fig_plotly_reg
-
Total running time of the script: (0 minutes 1.125 seconds)
+
Total running time of the script: (0 minutes 1.117 seconds)