mp_time_split.utils namespace

Submodules

mp_time_split.utils.api module

mp_time_split.utils.data module

mp_time_split.utils.data.get_discovery_dict(references: List[dict]) → List[dict]

Get a dictionary containing earliest bib info for each MP entry.

Modified from source: “How do I do a time-split of Materials Project entries? e.g. pre-2018 vs. post-2018” https://matsci.org/t/42584/4?u=sgbaird, answer by @Joseph_Montoya, Materials Project Alumni

Parameters:

provenance_results (List[dict]) – List of references results, e.g. taken from from the ProvenanceRester API results (mp_api.provenance())

Returns:

Dictionary containing earliest bib info for each MP entry with keys: ["year", "authors", "num_authors"]

Return type:

discovery, List[dict]

Examples

>>> with MPRester(api_key) as mpr:
...     provenance_results = mpr.provenance.search(num_sites=(1, 4), elements=["V"])
>>> discovery = get_discovery_dict(provenance_results)
[{'year': 1963, 'authors': ['Raub, E.', 'Fritzsche, W.'], 'num_authors': 2}, {'year': 1925, 'authors': ['Becker, K.', 'Ebert, F.'], 'num_authors': 2}, {'year': 1965, 'authors': ['Giessen, B.C.', 'Grant, N.J.'], 'num_authors': 2}, {'year': 1957, 'authors': ['Philip, T.V.', 'Beck, P.A.'], 'num_authors': 2}, {'year': 1963, 'authors': ['Darby, J.B.jr.'], 'num_authors': 1}, {'year': 1977, 'authors': ['Aksenova, T.V.', 'Kuprina, V.V.', 'Bernard, V.B.', 'Skolozdra, R.V.'], 'num_authors': 4}, {'year': 1964, 'authors': ['Maldonado, A.', 'Schubert, K.'], 'num_authors': 2}, {'year': 1962, 'authors': ['Darby, J.B.jr.', 'Lam, D.J.', 'Norton, L.J.', 'Downey, J.W.'], 'num_authors': 4}, {'year': 1925, 'authors': ['Becker, K.', 'Ebert, F.'], 'num_authors': 2}, {'year': 1959, 'authors': ['Dwight, A.E.'], 'num_authors': 1}] # noqa: E501

mp_time_split.utils.gen module

class mp_time_split.utils.gen.DummyGenerator

Bases: object

fit(inputs)

gen(n=100)

mp_time_split.utils.split module

class mp_time_split.utils.split.TimeKFold(n_splits=5, *, shuffle=False, random_state=None)

Bases: _BaseKFold

Time Series K-Folds cross-validator

TODO: update docstring

Provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).

Each fold is then used once as a validation while the k - 1 remaining folds form the training set.

Read more in the User Guide.

Parameters:

n_splits (int, default=5) –

Number of folds. Must be at least 2.

Changed in version 0.22: n_splits default value changed from 3 to 5.

Examples

>>> import numpy as np
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
...     print("TRAIN:", train_index, "TEST:", test_index)
...     X_train, X_test = X[train_index], X[test_index]
...     y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]

Notes

The first n_samples % n_splits folds have size n_samples // n_splits + 1, other folds have size n_samples // n_splits, where n_samples is the number of samples.

Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer.

See also

StratifiedKFold

Takes group information into account to avoid building folds with imbalanced class distributions (for binary or multiclass classification tasks).

GroupKFold

K-fold iterator variant with non-overlapping groups.

RepeatedKFold

Repeats K-Fold n times.

split(X, y=None, groups=None)

Generate indices to split data into training and test set.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.

• y (array-like of shape (n_samples,), default=None) – The target variable for supervised learning problems.

• groups (array-like of shape (n_samples,), default=None) – Group labels for the samples used while splitting the dataset into train/test set.

Yields:

• train (ndarray) – The training set indices for that split.

• test (ndarray) – The testing set indices for that split.

class mp_time_split.utils.split.TimeSeriesOverflowSplit(n_splits=5, *, max_train_size=None, test_size=None, gap=0)

Bases: _BaseKFold

Time Series cross-validator

TODO: update docstring

Provides train/test indices to split time series data samples that are observed at fixed time intervals, in train/test sets. In each split, test indices must be higher than before, and thus shuffling in cross validator is inappropriate.

This cross-validation object is a variation of KFold. In the kth split, it returns first k folds as train set and the (k+1)th fold as test set.

Note that unlike standard cross-validation methods, successive training sets are supersets of those that come before them.

Read more in the User Guide.

Added in version 0.18.

Parameters:

• n_splits (int, default=5) –

  Number of splits. Must be at least 2.

  Changed in version 0.22: n_splits default value changed from 3 to 5.

• max_train_size (int, default=None) – Maximum size for a single training set.

• test_size (int, default=None) –

  Used to limit the size of the test set. Defaults to n_samples // (n_splits + 1), which is the maximum allowed value with gap=0.

  Added in version 0.24.

• gap (int, default=0) –

  Number of samples to exclude from the end of each train set before the test set.

  Added in version 0.24.

Examples

>>> import numpy as np
>>> from sklearn.model_selection import TimeSeriesSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4, 5, 6])
>>> tscv = TimeSeriesSplit()
>>> print(tscv)
TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None)
>>> for train_index, test_index in tscv.split(X):
...     print("TRAIN:", train_index, "TEST:", test_index)
...     X_train, X_test = X[train_index], X[test_index]
...     y_train, y_test = y[train_index], y[test_index]
TRAIN: [0] TEST: [1]
TRAIN: [0 1] TEST: [2]
TRAIN: [0 1 2] TEST: [3]
TRAIN: [0 1 2 3] TEST: [4]
TRAIN: [0 1 2 3 4] TEST: [5]
>>> # Fix test_size to 2 with 12 samples
>>> X = np.random.randn(12, 2)
>>> y = np.random.randint(0, 2, 12)
>>> tscv = TimeSeriesSplit(n_splits=3, test_size=2)
>>> for train_index, test_index in tscv.split(X):
...    print("TRAIN:", train_index, "TEST:", test_index)
...    X_train, X_test = X[train_index], X[test_index]
...    y_train, y_test = y[train_index], y[test_index]
TRAIN: [0 1 2 3 4 5] TEST: [6 7]
TRAIN: [0 1 2 3 4 5 6 7] TEST: [8 9]
TRAIN: [0 1 2 3 4 5 6 7 8 9] TEST: [10 11]
>>> # Add in a 2 period gap
>>> tscv = TimeSeriesSplit(n_splits=3, test_size=2, gap=2)
>>> for train_index, test_index in tscv.split(X):
...    print("TRAIN:", train_index, "TEST:", test_index)
...    X_train, X_test = X[train_index], X[test_index]
...    y_train, y_test = y[train_index], y[test_index]
TRAIN: [0 1 2 3] TEST: [6 7]
TRAIN: [0 1 2 3 4 5] TEST: [8 9]
TRAIN: [0 1 2 3 4 5 6 7] TEST: [10 11]

Notes

The training set has size i * n_samples // (n_splits + 1) + n_samples % (n_splits + 1) in the i th split, with a test set of size n_samples//(n_splits + 1) by default, where n_samples is the number of samples.

split(X, y=None, groups=None)

Generate indices to split data into training and test set.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.

• y (array-like of shape (n_samples,)) – Always ignored, exists for compatibility.

• groups (array-like of shape (n_samples,)) – Always ignored, exists for compatibility.

Yields:

• train (ndarray) – The training set indices for that split.

• test (ndarray) – The testing set indices for that split.

mp_time_split.utils.split.mp_time_split(X, mode='TimeSeriesSplit', use_trainval_test: bool = True, n_cv_splits: int = 5, max_train_size=None, test_size=None, gap=0)