# Functions¶

## Summary¶

function |
class parent |
truncated documentation |
---|---|---|

Changes |
||

Compute label assignment and inertia for a dense array Return the inertia (sum of squared distances to the centers). … |
||

Compute label assignment and inertia for a CSR input Return the inertia (sum of squared distances to the centers). |
||

M step of the K-means EM algorithm Computation of cluster centers / means. |
||

M step of the K-means EM algorithm. Computation of cluster centers / means. |
||

M step of the K-means EM algorithm Computation of cluster centers / means. |
||

Computes all polynomial features combinations. |
||

Computes weights difference. |
||

Creates a matrix |
||

Completes the constraint k-means. |
||

Completes the constraint |
||

Completes the constraint |
||

Associates points to clusters. |
||

Runs KMeans iterator but weights cluster among them. |
||

Returns a unique column name not in the existing dataframe. |
||

Returns the tree object. |
||

Computes total weighted inertia. |
||

Compute the initial centroids |
||

Init n_clusters seeds according to k-means++ |
||

A single run of k-means, assumes preparation completed prior. |
||

E step of the K-means EM algorithm. Computes the labels and the inertia of the given samples and centers. This … |
||

Computes labels and inertia using a full distance matrix. This will overwrite the ‘distances’ array in-place. |
||

E step of the K-means EM algorithm. Compute the labels and the inertia of the given samples and centers. This will … |
||

Computes weighted inertia. It also adds a fraction of the whole inertia depending on how balanced the clusters are. … |
||

Internal function to convert a pipeline into some graph. |
||

Randomizes index depending on the value. Swap indexes. Modifies |
||

if this function is added to the module, the help automation and unit tests call it first before anything goes on … |
||

Tries to switch clusters. Modifies |
||

_test_criterion_check(Criterion criterion) |
||

_test_criterion_impurity_improvement(Criterion criterion, double impurity_parent, double impurity_left, double impurity_right) … |
||

_test_criterion_init(Criterion criterion, const DOUBLE_t[:, |
||

_test_criterion_node_impurity(Criterion criterion) Test purposes. Methods cannot be directly called from python. |
||

_test_criterion_node_impurity_children(Criterion criterion) Test purposes. Methods cannot be directly called from python. … |
||

_test_criterion_node_value(Criterion criterion) Test purposes. Methods cannot be directly called from python. |
||

_test_criterion_printf(Criterion crit) Test purposes. Methods cannot be directly called from python. |
||

_test_criterion_proxy_impurity_improvement(Criterion criterion) Test purposes. Methods cannot be directly called from python. … |
||

_test_criterion_update(Criterion criterion, SIZE_t new_pos) Test purposes. Methods cannot be directly called from python. … |
||

Return a tolerance which is independent of the dataset |
||

Computes the polynomial features |
||

Computes the polynomial features |
||

Computes the absolute loss for regression. |
||

Aggregates timeseries assuming the data is in a dataframe. |
||

Overwrite methods |
||

Generates articial data every minutes. |
||

assert_criterion_equal(Criterion c1, Criterion c2) |
||

Checks that two models are equal. |
||

Builds standard |
||

Checks the library is working. It raises an exception. If you want to disable the logs: |
||

Checks that datasets |
||

Clones an estimator with the fitted results. |
||

Completes the constraint k-means. |
||

Computes the predictions but tries to associates the same numbers of points in each cluster. |
||

dgelss(double[:, |
||

Enumerates all the models within a pipeline. |
||

Clusters times series to find similar patterns. |
||

Returns 1 if |
||

Formats a function call with named parameters. |
||

Formats a list of parameters. |
||

Formats a value to be included in a string. |
||

Tells if |
||

Tells if scikit-learn is more recent than 0.23. |
||

Computes where is . … |
||

Linearizes a matrix into a new one with 3 columns value, row, column. The output format is similar to :epkg:`csr_matrix` … |
||

Computes . |
||

Converts a machine learned model into a function which converts a vector into features produced by the model. It … |
||

Builds a featurizer from a keras model It returns a function which returns the output of one particular … |
||

Builds a featurizer from a :epkg:`scikit-learn:linear_model:LogisticRegression`. It returns a function which returns … |
||

Builds a featurizer from a :epkg:`scikit-learn:ensemble:RandomForestClassifier`. It returns a function which returns … |
||

Builds a featurizer from a torch model It returns a function which returns the output of one particular … |
||

Computes non linear correlations. |
||

Exports a |
||

Exports a |
||

Plots a gallery of images using matplotlib. |
||

Shows a timeseries dispatched by days as bars. |
||

Returns the leave every observations of |
||

Tests that a cloned model is similar to the original one. |
||

Creates a model, checks that a grid search works with it. |
||

Creates a model, fit, predict and check the prediction are similar after the model was pickled, unpickled. |
||

Splits into train and test data even if they are None. |
||

Finds the common node to nodes |
||

Lists nodes involved into the path to find node |
||

Returns the indices of every leave in a tree. |
||

The function determines which leaves are neighbors. The method uses some memory as it creates creates a grid of … |
||

Returns a dictionary |
||

Determines the ranges for a node all dimensions. |
||

Computes . It compares the prediction to what … |
||

Checks types and dimension. Calls |
||

Checks types and dimension. Calls |
||

Checks types and dimension. Calls |