.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/pitfalls_and_solutions/plot_skd013_train_test_time_overlap.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_pitfalls_and_solutions_plot_skd013_train_test_time_overlap.py: .. _example_skd013_train_test_time_overlap: SKD013 - Train-test overlap in time series ========================================== This example demonstrates mitigations when check :ref:`SKD013 ` fires on temporal data. The check compares datetime columns in train and test folds and flags overlap when the latest training timestamp is not strictly before the earliest test timestamp. Mitigations from the :ref:`automated_checks` user guide: - use a time-based splitter such as :class:`~sklearn.model_selection.TimeSeriesSplit` or similar. We use the employee salaries dataset ordered by hire date and predict whether salary exceeds the median. The goal is to evaluate on future hires only so test scores reflect forward-looking performance. .. GENERATED FROM PYTHON SOURCE LINES 24-33 Load the employee salaries dataset ================================== Rows are sorted by ``date_first_hired``. We expose hire dates as a pandas ``timestamp`` column because SKD013 requires a datetime dtype. Shuffling before a hold-out split mixes later hires into training; that is the pattern SKD013 is designed to catch. Use full :meth:`~skore.EstimatorReport.checks.summarize` on the trigger; ``fast_mode=True`` on fix cells. .. GENERATED FROM PYTHON SOURCE LINES 33-46 .. code-block:: Python import pandas as pd from skrub.datasets import fetch_employee_salaries dataset = fetch_employee_salaries() df = dataset.X.copy() df["current_annual_salary"] = dataset.y df["timestamp"] = pd.to_datetime(df["date_first_hired"]) df = df.sort_values("timestamp").reset_index(drop=True) y = (df["current_annual_salary"] > df["current_annual_salary"].median()).astype(int) X = df.drop(columns=["current_annual_salary"]) .. GENERATED FROM PYTHON SOURCE LINES 47-49 :class:`~skrub.TableReport` confirms chronological ordering and mixed HR features. .. GENERATED FROM PYTHON SOURCE LINES 49-54 .. code-block:: Python from skrub import TableReport TableReport(X) .. raw:: html

	gender	department	department_name	division	assignment_category	employee_position_title	date_first_hired	year_first_hired	timestamp
0	F	HHS	Department of Health and Human Services	Area Health Centers	Fulltime-Regular	Community Health Nurse II	09/30/1965	1,965	1965-09-30 00:00:00
1	F	HHS	Department of Health and Human Services	Child Welfare Services	Fulltime-Regular	Social Worker III	06/04/1967	1,967	1967-06-04 00:00:00
2	F	HHS	Department of Health and Human Services	Fiscal Team	Fulltime-Regular	Accountant/Auditor I	08/21/1967	1,967	1967-08-21 00:00:00
3	F	CCL	County Council	Council Central Staff	Fulltime-Regular	Legislative Analyst III	10/17/1967	1,967	1967-10-17 00:00:00
4	F	CEC	Community Engagement Cluster	Regional Services Centers	Fulltime-Regular	Manager I	06/21/1968	1,968	1968-06-21 00:00:00

9,223		HHS	Department of Health and Human Services	Emergency Housing Assistance	Fulltime-Regular	Social Worker II	12/27/2016	2,016	2016-12-27 00:00:00
9,224		REC	Department of Recreation	Neighborhood Senior Centers - Northern	Fulltime-Regular	Recreation Specialist	12/27/2016	2,016	2016-12-27 00:00:00
9,225		HHS	Department of Health and Human Services	Infants and Toddlers	Fulltime-Regular	Program Aide	12/27/2016	2,016	2016-12-27 00:00:00
9,226	F	POL	Department of Police	MSB Communications Division	Fulltime-Regular	Public Safety Communications Specialist I	12/27/2016	2,016	2016-12-27 00:00:00
9,227		HHS	Department of Health and Human Services	Infants and Toddlers	Fulltime-Regular	Program Aide	12/27/2016	2,016	2016-12-27 00:00:00

Column	Column name	dtype	Is sorted	Null values	Unique values	Mean	Std	Min	Median	Max
0	gender	ObjectDType	False	17 (0.2%)	2 (< 0.1%)
1	department	ObjectDType	False	0 (0.0%)	37 (0.4%)
2	department_name	ObjectDType	False	0 (0.0%)	37 (0.4%)
3	division	ObjectDType	False	0 (0.0%)	694 (7.5%)
4	assignment_category	ObjectDType	False	0 (0.0%)	2 (< 0.1%)
5	employee_position_title	ObjectDType	False	0 (0.0%)	443 (4.8%)
6	date_first_hired	ObjectDType	False	0 (0.0%)	2264 (24.5%)
7	year_first_hired	Int64DType	True	0 (0.0%)	51 (0.6%)	2.00e+03	9.33	1,965	2,005	2,016
8	timestamp	DateTime64DType	True	0 (0.0%)	2264 (24.5%)			1965-09-30T00:00:00		2016-12-27T00:00:00

Column 1	Column 2	Cramér's V
department	department_name	1.00
year_first_hired	timestamp	0.950
division	assignment_category	0.611
assignment_category	employee_position_title	0.518
department	employee_position_title	0.419
department_name	employee_position_title	0.419
division	employee_position_title	0.398
department_name	assignment_category	0.376
department	assignment_category	0.376
gender	department	0.364
gender	department_name	0.364
department_name	division	0.361
department	division	0.361
gender	employee_position_title	0.273
employee_position_title	date_first_hired	0.254
gender	assignment_category	0.246
gender	division	0.243
date_first_hired	timestamp	0.160
date_first_hired	year_first_hired	0.158
department	date_first_hired	0.150
department_name	date_first_hired	0.150
employee_position_title	timestamp	0.149
employee_position_title	year_first_hired	0.145
division	date_first_hired	0.103
gender	date_first_hired	0.102
department_name	timestamp	0.100
department	timestamp	0.100
department_name	year_first_hired	0.0962
department	year_first_hired	0.0962
gender	timestamp	0.0851
gender	year_first_hired	0.0806
assignment_category	date_first_hired	0.0801
division	timestamp	0.0724
division	year_first_hired	0.0720
assignment_category	timestamp	0.0593
assignment_category	year_first_hired	0.0543

Please enable javascript

The skrub table reports need javascript to display correctly. If you are displaying a report in a Jupyter notebook and you see this message, you may need to re-execute the cell or to trust the notebook (button on the top right or "File > Trust notebook").

.. GENERATED FROM PYTHON SOURCE LINES 55-57 The binary target marks above-median earners, a classification view of the salary column. .. GENERATED FROM PYTHON SOURCE LINES 57-60 .. code-block:: Python TableReport(y.to_frame(name="high_earner")) .. raw:: html

	high_earner
0	1
1	1
2	1
3	1
4	1

9,223	0
9,224	0
9,225	0
9,226	0
9,227	0

Column	Column name	dtype	Is sorted	Null values	Unique values	Mean	Std	Min	Median	Max
0	high_earner	Int64DType	False	0 (0.0%)	2 (< 0.1%)	0.500	0.500	0	0	1

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.. GENERATED FROM PYTHON SOURCE LINES 61-67 Trigger SKD013 - shuffled train/test split ========================================== :class:`~skore.TrainTestSplit` with ``shuffle=True`` randomizes row order before cutting folds, so future timestamps land in training. Fit a tabular classifier and summarize checks. .. GENERATED FROM PYTHON SOURCE LINES 67-82 .. code-block:: Python from skore import TrainTestSplit, evaluate from skrub import tabular_pipeline splitter_shuffled = TrainTestSplit(random_state=42, shuffle=True) report = evaluate( tabular_pipeline("classifier"), X=X, y=y, pos_label=1, splitter=splitter_shuffled, ) report .. raw:: html

Metric	HistGradientBoostingClassifier
Accuracy	0.938787
Precision	0.940529
Recall	0.935378
ROC AUC	0.987546
Log loss	0.144964
Brier score	0.044099
Fit time (s)	2.153153
Predict time (s)	0.258402

Pipeline(steps=[('tablevectorizer',
                     TableVectorizer(low_cardinality=ToCategorical())),
                    ('histgradientboostingclassifier',
                     HistGradientBoostingClassifier())])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Pipeline

?Documentation for PipelineiFitted

Parameters

	steps steps: list of tuples List of (name of step, estimator) tuples that are to be chained in sequential order. To be compatible with the scikit-learn API, all steps must define `fit`. All non-last steps must also define `transform`. See :ref:`Combining Estimators <combining_estimators>` for more details.	[('tablevectorizer', ...), ('histgradientboostingclassifier', ...)]
	transform_input transform_input: list of str, default=None The names of the :term:`metadata` parameters that should be transformed by the pipeline before passing it to the step consuming it. This enables transforming some input arguments to ``fit`` (other than ``X``) to be transformed by the steps of the pipeline up to the step which requires them. Requirement is defined via :ref:`metadata routing <metadata_routing>`. For instance, this can be used to pass a validation set through the pipeline. You can only set this if metadata routing is enabled, which you can enable using ``sklearn.set_config(enable_metadata_routing=True)``. .. versionadded:: 1.6	None
	memory memory: str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. See :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py` for an example on how to enable caching.	None
	verbose verbose: bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed.	False

Fitted attributes

Name	Type	Value
classes_ classes_: ndarray of shape (n_classes,) The classes labels. Only exist if the last step of the pipeline is a classifier.	ndarray[int64](2,)	[0,1]
feature_names_in_ feature_names_in_: ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 1.0	list	['gender', 'de...nt', 'de...me', 'di...on', ...]
n_features_in_ n_features_in_: int Number of features seen during :term:`fit`. Only defined if the underlying first estimator in `steps` exposes such an attribute when fit. .. versionadded:: 0.24	int	9

tablevectorizer: TableVectorizer

?Documentation for tablevectorizer: TableVectorizer

Parameters

	low_cardinality low_cardinality: transformer, "passthrough" or "drop", default=OneHotEncoder instance The transformer for string or categorical columns with strictly fewer than ``cardinality_threshold`` unique values. By default, we use a :class:`~sklearn.preprocessing.OneHotEncoder` that ignores unknown categories and drop one of the transformed columns if the feature contains only 2 categories.	ToCategorical()
	high_cardinality high_cardinality: transformer, "passthrough" or "drop", default=StringEncoder instance The transformer for string or categorical columns with at least ``cardinality_threshold`` unique values. The default is a :class:`~skrub.StringEncoder` with 30 components (30 output columns for each input). .. versionchanged:: 0.6.0 The default ``high_cardinality`` encoder has been changed from :class:`~skrub.GapEncoder` to :class:`~skrub.StringEncoder`.	StringEncoder()
	numeric numeric: transformer, "passthrough" or "drop", default="passthrough" The transformer for numeric columns (floats, ints, booleans).	PassThrough()
	datetime datetime: transformer, "passthrough" or "drop", default=DatetimeEncoder instance The transformer for date and datetime columns. By default, we use a :class:`~skrub.DatetimeEncoder`.	DatetimeEncoder()
	cardinality_threshold cardinality_threshold: int, default=40 String and categorical columns with a number of unique values strictly smaller than this threshold are handled by the transformer ``low_cardinality``, the rest are handled by the transformer ``high_cardinality``.	40
	specific_transformers specific_transformers: list of (transformer, list of column names) pairs, default=() Override the categories above for the given columns and force using the specified transformer. This disables any preprocessing usually done by the TableVectorizer; the columns are passed to the transformer without any modification. A column is not allowed to appear twice in ``specific_transformers``. Consider wrapping the ``TableVectorizer`` in :class:`~skrub.ApplyToCols` to select or exclude specific columns from the processing. Alternatively, the :ref:`skrub Data Ops <user_guide_data_ops_index>` allows for more complex pre-processing.	()
	drop_null_fraction drop_null_fraction: float or None, default=1.0 Fraction of null above which the column is dropped. If `drop_null_fraction` is set to ``1.0``, the column is dropped if it contains only nulls or NaNs (this is the default behavior). If `drop_null_fraction` is a number in ``[0.0, 1.0)``, the column is dropped if the fraction of nulls is strictly larger than `drop_null_fraction`. If `drop_null_fraction` is ``None``, this selection is disabled: no columns are dropped based on the number of null values they contain.	1.0
	drop_if_constant drop_if_constant: bool, default=False If set to true, drop columns that contain a single unique value. Note that missing values are considered as one additional distinct value.	False
	drop_if_unique drop_if_unique: bool, default=False If set to true, drop columns that contain only unique values, i.e., the number of unique values is equal to the number of rows in the column. Numeric columns are never dropped. .. deprecated:: 0.9.0 This functionality can drop informative columns and is unlikely to be of use in practice. It is therefore deprecated and will be removed in a future version.	False
	datetime_format datetime_format: str, default=None The format to use when parsing dates. If None, the format is inferred.	None
	null_strings null_strings: str or sequence of str, default=None Additional strings to consider as null values, beyond the default list.	None
	n_jobs n_jobs: int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a joblib ``parallel_backend`` context. ``-1`` means using all processors.	None

Fitted attributes

Name	Type	Value
all_outputs_ all_outputs_: list of str The names of the output columns.	list	['gender', 'de...nt', 'de...me', 'di...00', ...]
all_processing_steps_ all_processing_steps_: dict Maps the name of each column to a list of all the processing steps that were applied to it. Those steps may include some pre-processing transformations such as converting strings to datetimes or numbers, the main transformer (e.g. the :class:`~skrub.DatetimeEncoder`), and a post-processing step casting the main transformer's output to :obj:`numpy.float32`. See the "Examples" section below for details.	dict	{'as...ry': [CleanNullStrings(), DropUninformative(), ToStr(), ToCategorical()], 'da...ed': [CleanNullStrings(), DropUninformative(), ToDatetime(), DatetimeEncoder(), ...], 'de...nt': [CleanNullStrings(), DropUninformative(), ToStr(), ToCategorical()], 'de...me': [CleanNullStrings(), DropUninformative(), ToStr(), ToCategorical()], ...}
column_to_kind_ column_to_kind_: dict Maps each column name to the kind (``"high_cardinality"``, ``"low_cardinality"``, ``"specific"``, etc.) it was assigned.	dict	{'as...ry': 'lo...ty', 'da...ed': 'da...me', 'de...nt': 'lo...ty', 'de...me': 'lo...ty', ...}
feature_names_in_ feature_names_in_: list of str The names of the input columns, after applying some cleaning (casting all column names to strings and deduplication).	list	['gender', 'de...nt', 'de...me', 'di...on', ...]
input_to_outputs_ input_to_outputs_: dict Maps the name of each input column to the names of the corresponding output columns.	dict	{'as...ry': ['as...ry'], 'da...ed': ['da...ar', 'da...th', 'da...ay', 'da...ds'], 'de...nt': ['de...nt'], 'de...me': ['de...me'], ...}
kind_to_columns_ kind_to_columns_: dict The reverse of ``column_to_kind_``: maps each kind of column (``"high_cardinality"``, ``"low_cardinality"``, etc.) to a list of column names. For example ``kind_to_columns['datetime']`` contains the names of all datetime columns.	dict	{'da...me': ['da...ed', 'ti...mp'], 'hi...ty': ['di...on', 'em...le'], 'lo...ty': ['gender', 'de...nt', 'de...me', 'as...ry'], 'numeric': ['ye...ed'], ...}
n_features_in_ n_features_in_: int The number of input columns.	int	9
output_to_input_ output_to_input_: dict The reverse of ``input_to_outputs_``: maps the name of each output column to the name of the column in the input dataframe from which it was derived.	dict	{'as...ry': 'as...ry', 'da...ay': 'da...ed', 'da...th': 'da...ed', 'da...ds': 'da...ed', ...}
transformers_ transformers_: dict Maps the name of each column to the fitted transformer that was applied to it.	dict	{'as...ry': ToCategorical(), 'da...ed': DatetimeEncoder(), 'de...nt': ToCategorical(), 'de...me': ToCategorical(), ...}

numeric

['year_first_hired']

PassThrough

Parameters

datetime

['date_first_hired', 'timestamp']

DatetimeEncoder

?Documentation for DatetimeEncoder

Parameters

	resolution resolution: str or None, default="hour" If a string, extract up to this resolution. Must be "year", "month", "day", "hour", "minute", "second", "microsecond", or "nanosecond". For example, ``resolution="day"`` generates the features "year", "month", and "day" only. If the input column contains dates with no time information, time features ("hour", "minute", … ) are never extracted. If ``None``, the features listed above are not extracted (but day of the week and total seconds may still be extracted, see below).	'hour'
	add_weekday add_weekday: bool, default=False Extract the day of the week as a numerical feature from 1 (Monday) to 7 (Sunday).	False
	add_total_seconds add_total_seconds: bool, default=True Add the total number of seconds since the Unix epoch (00:00:00 UTC on 1 January 1970).	True
	add_day_of_year add_day_of_year: bool, default=False Add the day of year (ordinal day) as an integer in the range 1 to 365 (or 366 in the case of leap years). January 1st will be day 1, December 31st will be day 365 on non-leap years.	False
	periodic_encoding periodic_encoding: 'circular', 'spline', or None, default=None Add periodic features with different granularities. Add periodic features using either trigonometric (``circular``) or ``spline`` encoding. If ``None``, no periodic encoding is applied.	None

low_cardinality

['gender', 'department', 'department_name', 'assignment_category']

ToCategorical

?Documentation for ToCategorical

Parameters

high_cardinality

['division', 'employee_position_title']

StringEncoder

?Documentation for StringEncoder

Parameters

	n_components n_components: int, default=30 Number of components to be used for the singular value decomposition (SVD). Must be a positive integer.	30
	vectorizer vectorizer: str, "tfidf" or "hashing", default="tfidf" Vectorizer to apply to the strings, either `tfidf` or `hashing` for scikit-learn TfidfVectorizer or HashingVectorizer respectively.	'tfidf'
	ngram_range ngram_range: tuple of (int, int) pairs, default=(3,4) The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams.	(3, ...)
	analyzer analyzer: str, "char", "word" or "char_wb", default="char_wb" Whether the feature should be made of word or character n-grams. Option ``char_wb`` creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space.	'char_wb'
	stop_words stop_words: {'english'}, list, default=None If 'english', a built-in stop word list for English is used. There are several known issues with 'english' and you should consider an alternative (see `Using stop words <https://scikit-learn.org/stable/modules/feature_extraction.html#using-stop-words>`_). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. If None, no stop words will be used.	None
	random_state random_state: int, RandomState instance or None, default=None Used during randomized svd. Pass an int for reproducible results across multiple function calls.	None
	vocabulary vocabulary: Mapping or iterable, default=None In case of "tfidf" vectorizer, the vocabulary mapping passed to the vectorizer. Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms.	None

73 features

gender

department

department_name

division_00

division_01

division_02

division_03

division_04

division_05

division_06

division_07

division_08

division_09

division_10

division_11

division_12

division_13

division_14

division_15

division_16

division_17

division_18

division_19

division_20

division_21

division_22

division_23

division_24

division_25

division_26

division_27

division_28

division_29

assignment_category

employee_position_title_00

employee_position_title_01

employee_position_title_02

employee_position_title_03

employee_position_title_04

employee_position_title_05

employee_position_title_06

employee_position_title_07

employee_position_title_08

employee_position_title_09

employee_position_title_10

employee_position_title_11

employee_position_title_12

employee_position_title_13

employee_position_title_14

employee_position_title_15

employee_position_title_16

employee_position_title_17

employee_position_title_18

employee_position_title_19

employee_position_title_20

employee_position_title_21

employee_position_title_22

employee_position_title_23

employee_position_title_24

employee_position_title_25

employee_position_title_26

employee_position_title_27

employee_position_title_28

employee_position_title_29

date_first_hired_year

date_first_hired_month

date_first_hired_day

date_first_hired_total_seconds

year_first_hired

timestamp_year

timestamp_month

timestamp_day

timestamp_total_seconds

HistGradientBoostingClassifier

?Documentation for HistGradientBoostingClassifier

Parameters

	loss loss: {'log_loss'}, default='log_loss' The loss function to use in the boosting process. For binary classification problems, 'log_loss' is also known as logistic loss, binomial deviance or binary crossentropy. Internally, the model fits one tree per boosting iteration and uses the logistic sigmoid function (expit) as inverse link function to compute the predicted positive class probability. For multiclass classification problems, 'log_loss' is also known as multinomial deviance or categorical crossentropy. Internally, the model fits one tree per boosting iteration and per class and uses the softmax function as inverse link function to compute the predicted probabilities of the classes.	'log_loss'
	learning_rate learning_rate: float, default=0.1 The learning rate, also known as shrinkage. This is used as a multiplicative factor for the leaves values. Use ``1`` for no shrinkage.	0.1
	max_iter max_iter: int, default=100 The maximum number of iterations of the boosting process, i.e. the maximum number of trees for binary classification. For multiclass classification, `n_classes` trees per iteration are built.	100
	max_leaf_nodes max_leaf_nodes: int or None, default=31 The maximum number of leaves for each tree. Must be strictly greater than 1. If None, there is no maximum limit.	31
	max_depth max_depth: int or None, default=None The maximum depth of each tree. The depth of a tree is the number of edges to go from the root to the deepest leaf. Depth isn't constrained by default.	None
	min_samples_leaf min_samples_leaf: int, default=20 The minimum number of samples per leaf. For small datasets with less than a few hundred samples, it is recommended to lower this value since only very shallow trees would be built.	20
	l2_regularization l2_regularization: float, default=0 The L2 regularization parameter penalizing leaves with small hessians. Use ``0`` for no regularization (default).	0.0
	max_features max_features: float, default=1.0 Proportion of randomly chosen features in each and every node split. This is a form of regularization, smaller values make the trees weaker learners and might prevent overfitting. If interaction constraints from `interaction_cst` are present, only allowed features are taken into account for the subsampling. .. versionadded:: 1.4	1.0
	max_bins max_bins: int, default=255 The maximum number of bins to use for non-missing values. Before training, each feature of the input array `X` is binned into integer-valued bins, which allows for a much faster training stage. Features with a small number of unique values may use less than ``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin is always reserved for missing values. Must be no larger than 255.	255
	categorical_features categorical_features: array-like of {bool, int, str} of shape (n_features) or shape (n_categorical_features,), default='from_dtype' Indicates the categorical features. - None : no feature will be considered categorical. - boolean array-like : boolean mask indicating categorical features. - integer array-like : integer indices indicating categorical features. - str array-like: names of categorical features (assuming the training data has feature names). - `"from_dtype"`: dataframe columns with dtype "Categorical" and "Enum" are considered to be categorical features. The input must be a dataframe that is supported by narwhals (or supports it): :func:`narwhals.from_native` must work. This is the case, for instance, for pandas and polars DataFrames. For each categorical feature, there must be at most `max_bins` unique categories. Negative values for categorical features encoded as numeric dtypes are treated as missing values. All categorical values are converted to floating point numbers. This means that categorical values of 1.0 and 1 are treated as the same category. Read more in the :ref:`User Guide <categorical_support_gbdt>`. .. versionadded:: 0.24 .. versionchanged:: 1.2 Added support for feature names. .. versionchanged:: 1.4 Added `"from_dtype"` option. .. versionchanged:: 1.6 The default value changed from `None` to `"from_dtype"`.	'from_dtype'
	monotonic_cst monotonic_cst: array-like of int of shape (n_features) or dict, default=None Monotonic constraint to enforce on each feature are specified using the following integer values: - 1: monotonic increase - 0: no constraint - -1: monotonic decrease If a dict with str keys, map feature to monotonic constraints by name. If an array, the features are mapped to constraints by position. See :ref:`monotonic_cst_features_names` for a usage example. The constraints are only valid for binary classifications and hold over the probability of the positive class. Read more in the :ref:`User Guide <monotonic_cst_gbdt>`. .. versionadded:: 0.23 .. versionchanged:: 1.2 Accept dict of constraints with feature names as keys.	None
	interaction_cst interaction_cst: {"pairwise", "no_interactions"} or sequence of lists/tuples/sets of int, default=None Specify interaction constraints, the sets of features which can interact with each other in child node splits. Each item specifies the set of feature indices that are allowed to interact with each other. If there are more features than specified in these constraints, they are treated as if they were specified as an additional set. The strings "pairwise" and "no_interactions" are shorthands for allowing only pairwise or no interactions, respectively. For instance, with 5 features in total, `interaction_cst=[{0, 1}]` is equivalent to `interaction_cst=[{0, 1}, {2, 3, 4}]`, and specifies that each branch of a tree will either only split on features 0 and 1 or only split on features 2, 3 and 4. See :ref:`this example<ice-vs-pdp>` on how to use `interaction_cst`. .. versionadded:: 1.2	None
	warm_start warm_start: bool, default=False When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble. For results to be valid, the estimator should be re-trained on the same data only. See :term:`the Glossary <warm_start>`.	False
	early_stopping early_stopping: 'auto' or bool, default='auto' If 'auto', early stopping is enabled if the sample size is larger than 10000 or if `X_val` and `y_val` are passed to `fit`. If True, early stopping is enabled, otherwise early stopping is disabled. .. versionadded:: 0.23	'auto'
	scoring scoring: str or callable or None, default='loss' Scoring method to use for early stopping. Only used if `early_stopping` is enabled. Options: - str: see :ref:`scoring_string_names` for options. - callable: a scorer callable object (e.g., function) with signature ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details. - `None`: :ref:`accuracy <accuracy_score>` is used. - 'loss': early stopping is checked w.r.t the loss value.	'loss'
	validation_fraction validation_fraction: int or float or None, default=0.1 Proportion (or absolute size) of training data to set aside as validation data for early stopping. If None, early stopping is done on the training data. The value is ignored if either early stopping is not performed, e.g. `early_stopping=False`, or if `X_val` and `y_val` are passed to fit.	0.1
	n_iter_no_change n_iter_no_change: int, default=10 Used to determine when to "early stop". The fitting process is stopped when none of the last ``n_iter_no_change`` scores are better than the ``n_iter_no_change - 1`` -th-to-last one, up to some tolerance. Only used if early stopping is performed.	10
	tol tol: float, default=1e-7 The absolute tolerance to use when comparing scores. The higher the tolerance, the more likely we are to early stop: higher tolerance means that it will be harder for subsequent iterations to be considered an improvement upon the reference score.	1e-07
	verbose verbose: int, default=0 The verbosity level. If not zero, print some information about the fitting process. ``1`` prints only summary info, ``2`` prints info per iteration.	0
	random_state random_state: int, RandomState instance or None, default=None Pseudo-random number generator to control the subsampling in the binning process, and the train/validation data split if early stopping is enabled. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.	None
	class_weight class_weight: dict or 'balanced', default=None Weights associated with classes in the form `{class_label: weight}`. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as `n_samples / (n_classes * np.bincount(y))`. Note that these weights will be multiplied with sample_weight (passed through the fit method) if `sample_weight` is specified. .. versionadded:: 1.2	None

Fitted attributes

Name	Type	Value
classes_ classes_: array, shape = (n_classes,) Class labels.	ndarray[int64](2,)	[0,1]
do_early_stopping_ do_early_stopping_: bool Indicates whether early stopping is used during training.	bool	False
feature_names_in_ feature_names_in_: ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0	ndarray[object](73,)	['gender','department','department_name',...,'timestamp_month', 'timestamp_day','timestamp_total_seconds']
is_categorical_ is_categorical_: ndarray, shape (n_features, ) or None Boolean mask for the categorical features. ``None`` if there are no categorical features.	ndarray[bool](73,)	[ True, True, True,...,False,False,False]
n_features_in_ n_features_in_: int Number of features seen during :term:`fit`. .. versionadded:: 0.24	int	73
n_iter_ n_iter_: int The number of iterations as selected by early stopping, depending on the `early_stopping` parameter. Otherwise it corresponds to max_iter.	int	100
n_trees_per_iteration_ n_trees_per_iteration_: int The number of tree that are built at each iteration. This is equal to 1 for binary classification, and to ``n_classes`` for multiclass classification.	int	1
train_score_ train_score_: ndarray, shape (n_iter_+1,) The scores at each iteration on the training data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the ``scoring`` parameter. If ``scoring`` is not 'loss', scores are computed on a subset of at most 10 000 samples. Empty if no early stopping.	ndarray[float64](0,)	[]
validation_score_ validation_score_: ndarray, shape (n_iter_+1,) The scores at each iteration on the held-out validation data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the ``scoring`` parameter. Empty if no early stopping or if ``validation_fraction`` is None.	ndarray[float64](0,)	[]

	gender	department	department_name	division	assignment_category	employee_position_title	date_first_hired	year_first_hired	timestamp	current_annual_salary
0	M	DLC	Department of Liquor Control	Leisure World	Parttime-Regular	Liquor Store Clerk I	02/17/2008	2,008	2008-02-17 00:00:00	0
1	M	POL	Department of Police	MSB Communications Division	Fulltime-Regular	Manager II	02/01/1993	1,993	1993-02-01 00:00:00	1
2	F	HHS	Department of Health and Human Services	School Health Services	Parttime-Regular	Community Health Nurse II	08/06/2007	2,007	2007-08-06 00:00:00	1
3	M	FRS	Fire and Rescue Services	Station 30	Fulltime-Regular	Firefighter/Rescuer III	01/30/2006	2,006	2006-01-30 00:00:00	1
4	F	DGS	Department of General Services	Building Design and Construction	Fulltime-Regular	Senior Engineer	05/02/2005	2,005	2005-05-02 00:00:00	1

9,223	F	HHS	Department of Health and Human Services	Permanent Supportive Housing	Fulltime-Regular	Social Worker III	03/07/1994	1,994	1994-03-07 00:00:00	1
9,224	M	DLC	Department of Liquor Control	Beer Delivery Operations	Fulltime-Regular	Truck Driver Helper/Warehouse Worker	04/16/2008	2,008	2008-04-16 00:00:00	0
9,225	M	POL	Department of Police	PSB 5th District Patrol	Fulltime-Regular	Police Officer II	02/24/2014	2,014	2014-02-24 00:00:00	0
9,226	M	FRS	Fire and Rescue Services	Station 19	Fulltime-Regular	Firefighter/Rescuer II	03/10/2014	2,014	2014-03-10 00:00:00	0
9,227	F	DGS	Department of General Services	Building Design and Construction	Fulltime-Regular	Office Services Coordinator	08/14/2000	2,000	2000-08-14 00:00:00	0

Column	Column name	dtype	Is sorted	Null values	Unique values	Mean	Std	Min	Median	Max
0	gender	ObjectDType	False	17 (0.2%)	2 (< 0.1%)
1	department	ObjectDType	False	0 (0.0%)	37 (0.4%)
2	department_name	ObjectDType	False	0 (0.0%)	37 (0.4%)
3	division	ObjectDType	False	0 (0.0%)	694 (7.5%)
4	assignment_category	ObjectDType	False	0 (0.0%)	2 (< 0.1%)
5	employee_position_title	ObjectDType	False	0 (0.0%)	443 (4.8%)
6	date_first_hired	ObjectDType	False	0 (0.0%)	2264 (24.5%)
7	year_first_hired	Int64DType	False	0 (0.0%)	51 (0.6%)	2.00e+03	9.33	1,965	2,005	2,016
8	timestamp	DateTime64DType	False	0 (0.0%)	2264 (24.5%)			1965-09-30T00:00:00		2016-12-27T00:00:00
9	current_annual_salary	Int64DType	False	0 (0.0%)	2 (< 0.1%)	0.500	0.500	0	0	1

Please enable javascript

.. GENERATED FROM PYTHON SOURCE LINES 83-84 SKD013 should list ``timestamp`` as overlapping between train and test. .. GENERATED FROM PYTHON SOURCE LINES 84-87 .. code-block:: Python report.checks.summarize() .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 88-90 .. code-block:: Python report.metrics.summarize(data_source="both").frame(favorability=True) .. raw:: html

	HistGradientBoostingClassifier (train)	HistGradientBoostingClassifier (test)	favorability
metric
accuracy	0.986724	0.938787	(↗︎)
precision	0.989136	0.940529	(↗︎)
recall	0.984324	0.935378	(↗︎)
roc_auc	0.999248	0.987546	(↗︎)
log_loss	0.054044	0.144964	(↘︎)
brier_score	0.012418	0.044099	(↘︎)
fit_time	2.153153	2.153153	(↘︎)
predict_time	0.813124	0.258402	(↘︎)

.. GENERATED FROM PYTHON SOURCE LINES 91-93 Optimistic scores under shuffled splits are a leakage artifact; SKD013 forces you to respect time ordering before trusting metrics. .. GENERATED FROM PYTHON SOURCE LINES 95-100 Chronological hold-out ====================== ``shuffle=False`` keeps the test block as the latest rows in the table. No training row should carry a timestamp on or after the earliest test hire. .. GENERATED FROM PYTHON SOURCE LINES 100-112 .. code-block:: Python splitter_chrono = TrainTestSplit(random_state=42, shuffle=False) report_chrono = evaluate( tabular_pipeline("classifier"), X=X, y=y, pos_label=1, splitter=splitter_chrono, ) report_chrono .. raw:: html

Metric	HistGradientBoostingClassifier
Accuracy	0.924702
Precision	0.793956
Recall	0.818697
ROC AUC	0.974537
Log loss	0.163722
Brier score	0.051262
Fit time (s)	2.075071
Predict time (s)	0.279033

Pipeline(steps=[('tablevectorizer',
                     TableVectorizer(low_cardinality=ToCategorical())),
                    ('histgradientboostingclassifier',
                     HistGradientBoostingClassifier())])

Pipeline

?Documentation for PipelineiFitted

Parameters

	steps steps: list of tuples List of (name of step, estimator) tuples that are to be chained in sequential order. To be compatible with the scikit-learn API, all steps must define `fit`. All non-last steps must also define `transform`. See :ref:`Combining Estimators <combining_estimators>` for more details.	[('tablevectorizer', ...), ('histgradientboostingclassifier', ...)]
	transform_input transform_input: list of str, default=None The names of the :term:`metadata` parameters that should be transformed by the pipeline before passing it to the step consuming it. This enables transforming some input arguments to ``fit`` (other than ``X``) to be transformed by the steps of the pipeline up to the step which requires them. Requirement is defined via :ref:`metadata routing <metadata_routing>`. For instance, this can be used to pass a validation set through the pipeline. You can only set this if metadata routing is enabled, which you can enable using ``sklearn.set_config(enable_metadata_routing=True)``. .. versionadded:: 1.6	None
	memory memory: str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. See :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py` for an example on how to enable caching.	None
	verbose verbose: bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed.	False

Fitted attributes

Name	Type	Value
classes_ classes_: ndarray of shape (n_classes,) The classes labels. Only exist if the last step of the pipeline is a classifier.	ndarray[int64](2,)	[0,1]
feature_names_in_ feature_names_in_: ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 1.0	list	['gender', 'de...nt', 'de...me', 'di...on', ...]
n_features_in_ n_features_in_: int Number of features seen during :term:`fit`. Only defined if the underlying first estimator in `steps` exposes such an attribute when fit. .. versionadded:: 0.24	int	9

tablevectorizer: TableVectorizer

?Documentation for tablevectorizer: TableVectorizer

Parameters

	low_cardinality low_cardinality: transformer, "passthrough" or "drop", default=OneHotEncoder instance The transformer for string or categorical columns with strictly fewer than ``cardinality_threshold`` unique values. By default, we use a :class:`~sklearn.preprocessing.OneHotEncoder` that ignores unknown categories and drop one of the transformed columns if the feature contains only 2 categories.	ToCategorical()
	high_cardinality high_cardinality: transformer, "passthrough" or "drop", default=StringEncoder instance The transformer for string or categorical columns with at least ``cardinality_threshold`` unique values. The default is a :class:`~skrub.StringEncoder` with 30 components (30 output columns for each input). .. versionchanged:: 0.6.0 The default ``high_cardinality`` encoder has been changed from :class:`~skrub.GapEncoder` to :class:`~skrub.StringEncoder`.	StringEncoder()
	numeric numeric: transformer, "passthrough" or "drop", default="passthrough" The transformer for numeric columns (floats, ints, booleans).	PassThrough()
	datetime datetime: transformer, "passthrough" or "drop", default=DatetimeEncoder instance The transformer for date and datetime columns. By default, we use a :class:`~skrub.DatetimeEncoder`.	DatetimeEncoder()
	cardinality_threshold cardinality_threshold: int, default=40 String and categorical columns with a number of unique values strictly smaller than this threshold are handled by the transformer ``low_cardinality``, the rest are handled by the transformer ``high_cardinality``.	40
	specific_transformers specific_transformers: list of (transformer, list of column names) pairs, default=() Override the categories above for the given columns and force using the specified transformer. This disables any preprocessing usually done by the TableVectorizer; the columns are passed to the transformer without any modification. A column is not allowed to appear twice in ``specific_transformers``. Consider wrapping the ``TableVectorizer`` in :class:`~skrub.ApplyToCols` to select or exclude specific columns from the processing. Alternatively, the :ref:`skrub Data Ops <user_guide_data_ops_index>` allows for more complex pre-processing.	()
	drop_null_fraction drop_null_fraction: float or None, default=1.0 Fraction of null above which the column is dropped. If `drop_null_fraction` is set to ``1.0``, the column is dropped if it contains only nulls or NaNs (this is the default behavior). If `drop_null_fraction` is a number in ``[0.0, 1.0)``, the column is dropped if the fraction of nulls is strictly larger than `drop_null_fraction`. If `drop_null_fraction` is ``None``, this selection is disabled: no columns are dropped based on the number of null values they contain.	1.0
	drop_if_constant drop_if_constant: bool, default=False If set to true, drop columns that contain a single unique value. Note that missing values are considered as one additional distinct value.	False
	drop_if_unique drop_if_unique: bool, default=False If set to true, drop columns that contain only unique values, i.e., the number of unique values is equal to the number of rows in the column. Numeric columns are never dropped. .. deprecated:: 0.9.0 This functionality can drop informative columns and is unlikely to be of use in practice. It is therefore deprecated and will be removed in a future version.	False
	datetime_format datetime_format: str, default=None The format to use when parsing dates. If None, the format is inferred.	None
	null_strings null_strings: str or sequence of str, default=None Additional strings to consider as null values, beyond the default list.	None
	n_jobs n_jobs: int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a joblib ``parallel_backend`` context. ``-1`` means using all processors.	None

Fitted attributes

Name	Type	Value
all_outputs_ all_outputs_: list of str The names of the output columns.	list	['gender', 'de...nt', 'de...me', 'di...00', ...]
all_processing_steps_ all_processing_steps_: dict Maps the name of each column to a list of all the processing steps that were applied to it. Those steps may include some pre-processing transformations such as converting strings to datetimes or numbers, the main transformer (e.g. the :class:`~skrub.DatetimeEncoder`), and a post-processing step casting the main transformer's output to :obj:`numpy.float32`. See the "Examples" section below for details.	dict	{'as...ry': [CleanNullStrings(), DropUninformative(), ToStr(), ToCategorical()], 'da...ed': [CleanNullStrings(), DropUninformative(), ToDatetime(), DatetimeEncoder(), ...], 'de...nt': [CleanNullStrings(), DropUninformative(), ToStr(), ToCategorical()], 'de...me': [CleanNullStrings(), DropUninformative(), ToStr(), ToCategorical()], ...}
column_to_kind_ column_to_kind_: dict Maps each column name to the kind (``"high_cardinality"``, ``"low_cardinality"``, ``"specific"``, etc.) it was assigned.	dict	{'as...ry': 'lo...ty', 'da...ed': 'da...me', 'de...nt': 'lo...ty', 'de...me': 'lo...ty', ...}
feature_names_in_ feature_names_in_: list of str The names of the input columns, after applying some cleaning (casting all column names to strings and deduplication).	list	['gender', 'de...nt', 'de...me', 'di...on', ...]
input_to_outputs_ input_to_outputs_: dict Maps the name of each input column to the names of the corresponding output columns.	dict	{'as...ry': ['as...ry'], 'da...ed': ['da...ar', 'da...th', 'da...ay', 'da...ds'], 'de...nt': ['de...nt'], 'de...me': ['de...me'], ...}
kind_to_columns_ kind_to_columns_: dict The reverse of ``column_to_kind_``: maps each kind of column (``"high_cardinality"``, ``"low_cardinality"``, etc.) to a list of column names. For example ``kind_to_columns['datetime']`` contains the names of all datetime columns.	dict	{'da...me': ['da...ed', 'ti...mp'], 'hi...ty': ['di...on', 'em...le'], 'lo...ty': ['gender', 'de...nt', 'de...me', 'as...ry'], 'numeric': ['ye...ed'], ...}
n_features_in_ n_features_in_: int The number of input columns.	int	9
output_to_input_ output_to_input_: dict The reverse of ``input_to_outputs_``: maps the name of each output column to the name of the column in the input dataframe from which it was derived.	dict	{'as...ry': 'as...ry', 'da...ay': 'da...ed', 'da...th': 'da...ed', 'da...ds': 'da...ed', ...}
transformers_ transformers_: dict Maps the name of each column to the fitted transformer that was applied to it.	dict	{'as...ry': ToCategorical(), 'da...ed': DatetimeEncoder(), 'de...nt': ToCategorical(), 'de...me': ToCategorical(), ...}

numeric

['year_first_hired']

PassThrough

Parameters

datetime

['date_first_hired', 'timestamp']

DatetimeEncoder

?Documentation for DatetimeEncoder

Parameters

	resolution resolution: str or None, default="hour" If a string, extract up to this resolution. Must be "year", "month", "day", "hour", "minute", "second", "microsecond", or "nanosecond". For example, ``resolution="day"`` generates the features "year", "month", and "day" only. If the input column contains dates with no time information, time features ("hour", "minute", … ) are never extracted. If ``None``, the features listed above are not extracted (but day of the week and total seconds may still be extracted, see below).	'hour'
	add_weekday add_weekday: bool, default=False Extract the day of the week as a numerical feature from 1 (Monday) to 7 (Sunday).	False
	add_total_seconds add_total_seconds: bool, default=True Add the total number of seconds since the Unix epoch (00:00:00 UTC on 1 January 1970).	True
	add_day_of_year add_day_of_year: bool, default=False Add the day of year (ordinal day) as an integer in the range 1 to 365 (or 366 in the case of leap years). January 1st will be day 1, December 31st will be day 365 on non-leap years.	False
	periodic_encoding periodic_encoding: 'circular', 'spline', or None, default=None Add periodic features with different granularities. Add periodic features using either trigonometric (``circular``) or ``spline`` encoding. If ``None``, no periodic encoding is applied.	None

low_cardinality

['gender', 'department', 'department_name', 'assignment_category']

ToCategorical

?Documentation for ToCategorical

Parameters

high_cardinality

['division', 'employee_position_title']

StringEncoder

?Documentation for StringEncoder

Parameters

	n_components n_components: int, default=30 Number of components to be used for the singular value decomposition (SVD). Must be a positive integer.	30
	vectorizer vectorizer: str, "tfidf" or "hashing", default="tfidf" Vectorizer to apply to the strings, either `tfidf` or `hashing` for scikit-learn TfidfVectorizer or HashingVectorizer respectively.	'tfidf'
	ngram_range ngram_range: tuple of (int, int) pairs, default=(3,4) The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams.	(3, ...)
	analyzer analyzer: str, "char", "word" or "char_wb", default="char_wb" Whether the feature should be made of word or character n-grams. Option ``char_wb`` creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space.	'char_wb'
	stop_words stop_words: {'english'}, list, default=None If 'english', a built-in stop word list for English is used. There are several known issues with 'english' and you should consider an alternative (see `Using stop words <https://scikit-learn.org/stable/modules/feature_extraction.html#using-stop-words>`_). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. If None, no stop words will be used.	None
	random_state random_state: int, RandomState instance or None, default=None Used during randomized svd. Pass an int for reproducible results across multiple function calls.	None
	vocabulary vocabulary: Mapping or iterable, default=None In case of "tfidf" vectorizer, the vocabulary mapping passed to the vectorizer. Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms.	None

73 features

gender

department

department_name

division_00

division_01

division_02

division_03

division_04

division_05

division_06

division_07

division_08

division_09

division_10

division_11

division_12

division_13

division_14

division_15

division_16

division_17

division_18

division_19

division_20

division_21

division_22

division_23

division_24

division_25

division_26

division_27

division_28

division_29

assignment_category

employee_position_title_00

employee_position_title_01

employee_position_title_02

employee_position_title_03

employee_position_title_04

employee_position_title_05

employee_position_title_06

employee_position_title_07

employee_position_title_08

employee_position_title_09

employee_position_title_10

employee_position_title_11

employee_position_title_12

employee_position_title_13

employee_position_title_14

employee_position_title_15

employee_position_title_16

employee_position_title_17

employee_position_title_18

employee_position_title_19

employee_position_title_20

employee_position_title_21

employee_position_title_22

employee_position_title_23

employee_position_title_24

employee_position_title_25

employee_position_title_26

employee_position_title_27

employee_position_title_28

employee_position_title_29

date_first_hired_year

date_first_hired_month

date_first_hired_day

date_first_hired_total_seconds

year_first_hired

timestamp_year

timestamp_month

timestamp_day

timestamp_total_seconds

HistGradientBoostingClassifier

?Documentation for HistGradientBoostingClassifier

Parameters

	loss loss: {'log_loss'}, default='log_loss' The loss function to use in the boosting process. For binary classification problems, 'log_loss' is also known as logistic loss, binomial deviance or binary crossentropy. Internally, the model fits one tree per boosting iteration and uses the logistic sigmoid function (expit) as inverse link function to compute the predicted positive class probability. For multiclass classification problems, 'log_loss' is also known as multinomial deviance or categorical crossentropy. Internally, the model fits one tree per boosting iteration and per class and uses the softmax function as inverse link function to compute the predicted probabilities of the classes.	'log_loss'
	learning_rate learning_rate: float, default=0.1 The learning rate, also known as shrinkage. This is used as a multiplicative factor for the leaves values. Use ``1`` for no shrinkage.	0.1
	max_iter max_iter: int, default=100 The maximum number of iterations of the boosting process, i.e. the maximum number of trees for binary classification. For multiclass classification, `n_classes` trees per iteration are built.	100
	max_leaf_nodes max_leaf_nodes: int or None, default=31 The maximum number of leaves for each tree. Must be strictly greater than 1. If None, there is no maximum limit.	31
	max_depth max_depth: int or None, default=None The maximum depth of each tree. The depth of a tree is the number of edges to go from the root to the deepest leaf. Depth isn't constrained by default.	None
	min_samples_leaf min_samples_leaf: int, default=20 The minimum number of samples per leaf. For small datasets with less than a few hundred samples, it is recommended to lower this value since only very shallow trees would be built.	20
	l2_regularization l2_regularization: float, default=0 The L2 regularization parameter penalizing leaves with small hessians. Use ``0`` for no regularization (default).	0.0
	max_features max_features: float, default=1.0 Proportion of randomly chosen features in each and every node split. This is a form of regularization, smaller values make the trees weaker learners and might prevent overfitting. If interaction constraints from `interaction_cst` are present, only allowed features are taken into account for the subsampling. .. versionadded:: 1.4	1.0
	max_bins max_bins: int, default=255 The maximum number of bins to use for non-missing values. Before training, each feature of the input array `X` is binned into integer-valued bins, which allows for a much faster training stage. Features with a small number of unique values may use less than ``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin is always reserved for missing values. Must be no larger than 255.	255
	categorical_features categorical_features: array-like of {bool, int, str} of shape (n_features) or shape (n_categorical_features,), default='from_dtype' Indicates the categorical features. - None : no feature will be considered categorical. - boolean array-like : boolean mask indicating categorical features. - integer array-like : integer indices indicating categorical features. - str array-like: names of categorical features (assuming the training data has feature names). - `"from_dtype"`: dataframe columns with dtype "Categorical" and "Enum" are considered to be categorical features. The input must be a dataframe that is supported by narwhals (or supports it): :func:`narwhals.from_native` must work. This is the case, for instance, for pandas and polars DataFrames. For each categorical feature, there must be at most `max_bins` unique categories. Negative values for categorical features encoded as numeric dtypes are treated as missing values. All categorical values are converted to floating point numbers. This means that categorical values of 1.0 and 1 are treated as the same category. Read more in the :ref:`User Guide <categorical_support_gbdt>`. .. versionadded:: 0.24 .. versionchanged:: 1.2 Added support for feature names. .. versionchanged:: 1.4 Added `"from_dtype"` option. .. versionchanged:: 1.6 The default value changed from `None` to `"from_dtype"`.	'from_dtype'
	monotonic_cst monotonic_cst: array-like of int of shape (n_features) or dict, default=None Monotonic constraint to enforce on each feature are specified using the following integer values: - 1: monotonic increase - 0: no constraint - -1: monotonic decrease If a dict with str keys, map feature to monotonic constraints by name. If an array, the features are mapped to constraints by position. See :ref:`monotonic_cst_features_names` for a usage example. The constraints are only valid for binary classifications and hold over the probability of the positive class. Read more in the :ref:`User Guide <monotonic_cst_gbdt>`. .. versionadded:: 0.23 .. versionchanged:: 1.2 Accept dict of constraints with feature names as keys.	None
	interaction_cst interaction_cst: {"pairwise", "no_interactions"} or sequence of lists/tuples/sets of int, default=None Specify interaction constraints, the sets of features which can interact with each other in child node splits. Each item specifies the set of feature indices that are allowed to interact with each other. If there are more features than specified in these constraints, they are treated as if they were specified as an additional set. The strings "pairwise" and "no_interactions" are shorthands for allowing only pairwise or no interactions, respectively. For instance, with 5 features in total, `interaction_cst=[{0, 1}]` is equivalent to `interaction_cst=[{0, 1}, {2, 3, 4}]`, and specifies that each branch of a tree will either only split on features 0 and 1 or only split on features 2, 3 and 4. See :ref:`this example<ice-vs-pdp>` on how to use `interaction_cst`. .. versionadded:: 1.2	None
	warm_start warm_start: bool, default=False When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble. For results to be valid, the estimator should be re-trained on the same data only. See :term:`the Glossary <warm_start>`.	False
	early_stopping early_stopping: 'auto' or bool, default='auto' If 'auto', early stopping is enabled if the sample size is larger than 10000 or if `X_val` and `y_val` are passed to `fit`. If True, early stopping is enabled, otherwise early stopping is disabled. .. versionadded:: 0.23	'auto'
	scoring scoring: str or callable or None, default='loss' Scoring method to use for early stopping. Only used if `early_stopping` is enabled. Options: - str: see :ref:`scoring_string_names` for options. - callable: a scorer callable object (e.g., function) with signature ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details. - `None`: :ref:`accuracy <accuracy_score>` is used. - 'loss': early stopping is checked w.r.t the loss value.	'loss'
	validation_fraction validation_fraction: int or float or None, default=0.1 Proportion (or absolute size) of training data to set aside as validation data for early stopping. If None, early stopping is done on the training data. The value is ignored if either early stopping is not performed, e.g. `early_stopping=False`, or if `X_val` and `y_val` are passed to fit.	0.1
	n_iter_no_change n_iter_no_change: int, default=10 Used to determine when to "early stop". The fitting process is stopped when none of the last ``n_iter_no_change`` scores are better than the ``n_iter_no_change - 1`` -th-to-last one, up to some tolerance. Only used if early stopping is performed.	10
	tol tol: float, default=1e-7 The absolute tolerance to use when comparing scores. The higher the tolerance, the more likely we are to early stop: higher tolerance means that it will be harder for subsequent iterations to be considered an improvement upon the reference score.	1e-07
	verbose verbose: int, default=0 The verbosity level. If not zero, print some information about the fitting process. ``1`` prints only summary info, ``2`` prints info per iteration.	0
	random_state random_state: int, RandomState instance or None, default=None Pseudo-random number generator to control the subsampling in the binning process, and the train/validation data split if early stopping is enabled. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.	None
	class_weight class_weight: dict or 'balanced', default=None Weights associated with classes in the form `{class_label: weight}`. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as `n_samples / (n_classes * np.bincount(y))`. Note that these weights will be multiplied with sample_weight (passed through the fit method) if `sample_weight` is specified. .. versionadded:: 1.2	None

Fitted attributes

Name	Type	Value
classes_ classes_: array, shape = (n_classes,) Class labels.	ndarray[int64](2,)	[0,1]
do_early_stopping_ do_early_stopping_: bool Indicates whether early stopping is used during training.	bool	False
feature_names_in_ feature_names_in_: ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0	ndarray[object](73,)	['gender','department','department_name',...,'timestamp_month', 'timestamp_day','timestamp_total_seconds']
is_categorical_ is_categorical_: ndarray, shape (n_features, ) or None Boolean mask for the categorical features. ``None`` if there are no categorical features.	ndarray[bool](73,)	[ True, True, True,...,False,False,False]
n_features_in_ n_features_in_: int Number of features seen during :term:`fit`. .. versionadded:: 0.24	int	73
n_iter_ n_iter_: int The number of iterations as selected by early stopping, depending on the `early_stopping` parameter. Otherwise it corresponds to max_iter.	int	100
n_trees_per_iteration_ n_trees_per_iteration_: int The number of tree that are built at each iteration. This is equal to 1 for binary classification, and to ``n_classes`` for multiclass classification.	int	1
train_score_ train_score_: ndarray, shape (n_iter_+1,) The scores at each iteration on the training data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the ``scoring`` parameter. If ``scoring`` is not 'loss', scores are computed on a subset of at most 10 000 samples. Empty if no early stopping.	ndarray[float64](0,)	[]
validation_score_ validation_score_: ndarray, shape (n_iter_+1,) The scores at each iteration on the held-out validation data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the ``scoring`` parameter. Empty if no early stopping or if ``validation_fraction`` is None.	ndarray[float64](0,)	[]

	gender	department	department_name	division	assignment_category	employee_position_title	date_first_hired	year_first_hired	timestamp	current_annual_salary
0	F	HHS	Department of Health and Human Services	Area Health Centers	Fulltime-Regular	Community Health Nurse II	09/30/1965	1,965	1965-09-30 00:00:00	1
1	F	HHS	Department of Health and Human Services	Child Welfare Services	Fulltime-Regular	Social Worker III	06/04/1967	1,967	1967-06-04 00:00:00	1
2	F	HHS	Department of Health and Human Services	Fiscal Team	Fulltime-Regular	Accountant/Auditor I	08/21/1967	1,967	1967-08-21 00:00:00	1
3	F	CCL	County Council	Council Central Staff	Fulltime-Regular	Legislative Analyst III	10/17/1967	1,967	1967-10-17 00:00:00	1
4	F	CEC	Community Engagement Cluster	Regional Services Centers	Fulltime-Regular	Manager I	06/21/1968	1,968	1968-06-21 00:00:00	1

9,223		HHS	Department of Health and Human Services	Emergency Housing Assistance	Fulltime-Regular	Social Worker II	12/27/2016	2,016	2016-12-27 00:00:00	0
9,224		REC	Department of Recreation	Neighborhood Senior Centers - Northern	Fulltime-Regular	Recreation Specialist	12/27/2016	2,016	2016-12-27 00:00:00	0
9,225		HHS	Department of Health and Human Services	Infants and Toddlers	Fulltime-Regular	Program Aide	12/27/2016	2,016	2016-12-27 00:00:00	0
9,226	F	POL	Department of Police	MSB Communications Division	Fulltime-Regular	Public Safety Communications Specialist I	12/27/2016	2,016	2016-12-27 00:00:00	0
9,227		HHS	Department of Health and Human Services	Infants and Toddlers	Fulltime-Regular	Program Aide	12/27/2016	2,016	2016-12-27 00:00:00	0

Column	Column name	dtype	Is sorted	Null values	Unique values	Mean	Std	Min	Median	Max
0	gender	ObjectDType	False	17 (0.2%)	2 (< 0.1%)
1	department	ObjectDType	False	0 (0.0%)	37 (0.4%)
2	department_name	ObjectDType	False	0 (0.0%)	37 (0.4%)
3	division	ObjectDType	False	0 (0.0%)	694 (7.5%)
4	assignment_category	ObjectDType	False	0 (0.0%)	2 (< 0.1%)
5	employee_position_title	ObjectDType	False	0 (0.0%)	443 (4.8%)
6	date_first_hired	ObjectDType	False	0 (0.0%)	2264 (24.5%)
7	year_first_hired	Int64DType	True	0 (0.0%)	51 (0.6%)	2.00e+03	9.33	1,965	2,005	2,016
8	timestamp	DateTime64DType	True	0 (0.0%)	2264 (24.5%)			1965-09-30T00:00:00		2016-12-27T00:00:00
9	current_annual_salary	Int64DType	False	0 (0.0%)	2 (< 0.1%)	0.500	0.500	0	0	1

Please enable javascript

.. GENERATED FROM PYTHON SOURCE LINES 113-114 SKD013 should be absent; test rows are strictly after train rows. .. GENERATED FROM PYTHON SOURCE LINES 114-117 .. code-block:: Python report_chrono.checks.summarize(fast_mode=True) .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 118-120 .. code-block:: Python report_chrono.metrics.summarize(data_source="both").frame(favorability=True) .. raw:: html

	HistGradientBoostingClassifier (train)	HistGradientBoostingClassifier (test)	favorability
metric
accuracy	0.986589	0.924702	(↗︎)
precision	0.988724	0.793956	(↗︎)
recall	0.988028	0.818697	(↗︎)
roc_auc	0.999165	0.974537	(↗︎)
log_loss	0.054945	0.163722	(↘︎)
brier_score	0.012743	0.051262	(↘︎)
fit_time	2.075071	2.075071	(↘︎)
predict_time	0.799283	0.279033	(↘︎)

.. GENERATED FROM PYTHON SOURCE LINES 121-123 A simple chronological hold-out is often enough for deployment monitoring when you score on the most recent period. .. GENERATED FROM PYTHON SOURCE LINES 125-135 TimeSeriesSplit for cross-validated evaluation ============================================== :class:`~sklearn.model_selection.TimeSeriesSplit` trains on past rows and tests on the next chunk in each fold. Many employees share the same hire date, so a default split can still place the same calendar day in train and test at the fold boundary (SKD013 uses ``>=``). A small ``gap`` skips rows between folds and clears that tie. Early folds are small and unrelated checks such as SKD008 may warn on encoded features; we ignore SKD008 here to focus on temporal validity. .. GENERATED FROM PYTHON SOURCE LINES 135-149 .. code-block:: Python from sklearn.model_selection import TimeSeriesSplit splitter_tscv = TimeSeriesSplit(n_splits=5, gap=50) report_tscv = evaluate( tabular_pipeline("classifier"), X=X, y=y, pos_label=1, splitter=splitter_tscv, ) report_tscv .. raw:: html

Metric	mean	std
Accuracy	0.904031	0.048435
Precision	0.830040	0.094264
Recall	0.915221	0.060868
ROC AUC	0.945469	0.032899
Log loss	0.337386	0.178891
Brier score	0.080121	0.041890
Fit time (s)	1.709548	0.455250
Predict time (s)	0.261735	0.065330

Pipeline(steps=[('tablevectorizer',
                     TableVectorizer(low_cardinality=ToCategorical())),
                    ('histgradientboostingclassifier',
                     HistGradientBoostingClassifier())])

	gender	department	department_name	division	assignment_category	employee_position_title	date_first_hired	year_first_hired	timestamp	current_annual_salary
0	F	HHS	Department of Health and Human Services	Area Health Centers	Fulltime-Regular	Community Health Nurse II	09/30/1965	1,965	1965-09-30 00:00:00	1
1	F	HHS	Department of Health and Human Services	Child Welfare Services	Fulltime-Regular	Social Worker III	06/04/1967	1,967	1967-06-04 00:00:00	1
2	F	HHS	Department of Health and Human Services	Fiscal Team	Fulltime-Regular	Accountant/Auditor I	08/21/1967	1,967	1967-08-21 00:00:00	1
3	F	CCL	County Council	Council Central Staff	Fulltime-Regular	Legislative Analyst III	10/17/1967	1,967	1967-10-17 00:00:00	1
4	F	CEC	Community Engagement Cluster	Regional Services Centers	Fulltime-Regular	Manager I	06/21/1968	1,968	1968-06-21 00:00:00	1

9,223		HHS	Department of Health and Human Services	Emergency Housing Assistance	Fulltime-Regular	Social Worker II	12/27/2016	2,016	2016-12-27 00:00:00	0
9,224		REC	Department of Recreation	Neighborhood Senior Centers - Northern	Fulltime-Regular	Recreation Specialist	12/27/2016	2,016	2016-12-27 00:00:00	0
9,225		HHS	Department of Health and Human Services	Infants and Toddlers	Fulltime-Regular	Program Aide	12/27/2016	2,016	2016-12-27 00:00:00	0
9,226	F	POL	Department of Police	MSB Communications Division	Fulltime-Regular	Public Safety Communications Specialist I	12/27/2016	2,016	2016-12-27 00:00:00	0
9,227		HHS	Department of Health and Human Services	Infants and Toddlers	Fulltime-Regular	Program Aide	12/27/2016	2,016	2016-12-27 00:00:00	0

Column	Column name	dtype	Is sorted	Null values	Unique values	Mean	Std	Min	Median	Max
0	gender	ObjectDType	False	17 (0.2%)	2 (< 0.1%)
1	department	ObjectDType	False	0 (0.0%)	37 (0.4%)
2	department_name	ObjectDType	False	0 (0.0%)	37 (0.4%)
3	division	ObjectDType	False	0 (0.0%)	694 (7.5%)
4	assignment_category	ObjectDType	False	0 (0.0%)	2 (< 0.1%)
5	employee_position_title	ObjectDType	False	0 (0.0%)	443 (4.8%)
6	date_first_hired	ObjectDType	False	0 (0.0%)	2264 (24.5%)
7	year_first_hired	Int64DType	True	0 (0.0%)	51 (0.6%)	2.00e+03	9.33	1,965	2,005	2,016
8	timestamp	DateTime64DType	True	0 (0.0%)	2264 (24.5%)			1965-09-30T00:00:00		2016-12-27T00:00:00
9	current_annual_salary	Int64DType	False	0 (0.0%)	2 (< 0.1%)	0.500	0.500	0	0	1

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.. GENERATED FROM PYTHON SOURCE LINES 150-152 SKD013 should be absent; no fold trains on timestamps on or after its test block. .. GENERATED FROM PYTHON SOURCE LINES 152-155 .. code-block:: Python report_tscv.checks.summarize(fast_mode=True, ignore=["SKD008"]) .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 156-158 .. code-block:: Python report_tscv.metrics.summarize(data_source="both").frame(aggregate="mean") .. raw:: html

	histgradientboostingclassifier_(train)_mean	histgradientboostingclassifier_(test)_mean
metric
accuracy	0.995496	0.904031
precision	0.996858	0.830040
recall	0.995612	0.915221
roc_auc	0.999742	0.945469
log_loss	0.023280	0.337386
brier_score	0.004806	0.080121
fit_time	1.709548	1.709548
predict_time	0.592657	0.261735

.. GENERATED FROM PYTHON SOURCE LINES 159-161 Time-series cross-validation estimates stability across multiple forward windows, the right tool when a single hold-out is too noisy. .. GENERATED FROM PYTHON SOURCE LINES 163-170 Conclusion ========== SKD013 protects against training on the future. In this walkthrough, disabling shuffle and adopting ``TimeSeriesSplit`` aligned evaluation with how salary models are deployed over newly hired employees. Always pair temporal splits with features available at prediction time. .. rst-class:: sphx-glr-timing **Total running time of the script:** (1 minutes 17.681 seconds) .. _sphx_glr_download_auto_examples_pitfalls_and_solutions_plot_skd013_train_test_time_overlap.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_skd013_train_test_time_overlap.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_skd013_train_test_time_overlap.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_skd013_train_test_time_overlap.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_