val get_py : string -> Py.Object.tGet an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.
module IsotonicRegression : sig ... endval check_array :
?accept_sparse:[ `S of string | `StringList of string list | `Bool of bool ] ->
?accept_large_sparse:bool ->
?dtype:
[ `S of string
| `Dtype of Np.Dtype.t
| `Dtypes of Np.Dtype.t list
| `None ] ->
?order:[ `C | `F ] ->
?copy:bool ->
?force_all_finite:[ `Allow_nan | `Bool of bool ] ->
?ensure_2d:bool ->
?allow_nd:bool ->
?ensure_min_samples:int ->
?ensure_min_features:int ->
?estimator:[> `BaseEstimator ] Np.Obj.t ->
array:Py.Object.t ->
unit ->
Py.Object.tInput validation on an array, list, sparse matrix or similar.
By default, the input is checked to be a non-empty 2D array containing only finite values. If the dtype of the array is object, attempt converting to float, raising on failure.
Parameters ---------- array : object Input object to check / convert.
accept_sparse : string, boolean or list/tuple of strings (default=False) Strings representing allowed sparse matrix formats, such as 'csc', 'csr', etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error.
accept_large_sparse : bool (default=True) If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse=False will cause it to be accepted only if its indices are stored with a 32-bit dtype.
.. versionadded:: 0.20
dtype : string, type, list of types or None (default='numeric') Data type of result. If None, the dtype of the input is preserved. If 'numeric', dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list.
order : 'F', 'C' or None (default=None) Whether an array will be forced to be fortran or c-style. When order is None (default), then if copy=False, nothing is ensured about the memory layout of the output array; otherwise (copy=True) the memory layout of the returned array is kept as close as possible to the original array.
copy : boolean (default=False) Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.
force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf, np.nan, pd.NA in array. The possibilities are:
.. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``.
.. versionchanged:: 0.23 Accepts `pd.NA` and converts it into `np.nan`
ensure_2d : boolean (default=True) Whether to raise a value error if array is not 2D.
allow_nd : boolean (default=False) Whether to allow array.ndim > 2.
ensure_min_samples : int (default=1) Make sure that the array has a minimum number of samples in its first axis (rows for a 2D array). Setting to 0 disables this check.
ensure_min_features : int (default=1) Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when the input data has effectively 2 dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0 disables this check.
estimator : str or estimator instance (default=None) If passed, include the name of the estimator in warning messages.
Returns ------- array_converted : object The converted and validated array.
val check_consistent_length : Py.Object.t list -> Py.Object.tCheck that all arrays have consistent first dimensions.
Checks whether all objects in arrays have the same shape or length.
Parameters ---------- *arrays : list or tuple of input objects. Objects that will be checked for consistent length.
Determine whether y is monotonically correlated with x.
y is found increasing or decreasing with respect to x based on a Spearman correlation test.
Parameters ---------- x : array-like of shape (n_samples,) Training data.
y : array-like of shape (n_samples,) Training target.
Returns ------- increasing_bool : boolean Whether the relationship is increasing or decreasing.
Notes ----- The Spearman correlation coefficient is estimated from the data, and the sign of the resulting estimate is used as the result.
In the event that the 95% confidence interval based on Fisher transform spans zero, a warning is raised.
References ---------- Fisher transformation. Wikipedia. https://en.wikipedia.org/wiki/Fisher_transformation
val isotonic_regression :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
?y_min:float ->
?y_max:float ->
?increasing:bool ->
y:[> `ArrayLike ] Np.Obj.t ->
unit ->
float listSolve the isotonic regression model.
Read more in the :ref:`User Guide <isotonic>`.
Parameters ---------- y : array-like of shape (n_samples,) The data.
sample_weight : array-like of shape (n_samples,), default=None Weights on each point of the regression. If None, weight is set to 1 (equal weights).
y_min : float, default=None Lower bound on the lowest predicted value (the minimum value may still be higher). If not set, defaults to -inf.
y_max : float, default=None Upper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf.
increasing : boolean, optional, default: True Whether to compute ``y_`` is increasing (if set to True) or decreasing (if set to False)
Returns ------- y_ : list of floats Isotonic fit of y.
References ---------- 'Active set algorithms for isotonic regression; A unifying framework' by Michael J. Best and Nilotpal Chakravarti, section 3.
val spearmanr :
?b:Py.Object.t ->
?axis:[ `I of int | `None ] ->
?nan_policy:[ `Propagate | `Raise | `Omit ] ->
a:Py.Object.t ->
unit ->
Py.Object.t * floatCalculate a Spearman correlation coefficient with associated p-value.
The Spearman rank-order correlation coefficient is a nonparametric measure of the monotonicity of the relationship between two datasets. Unlike the Pearson correlation, the Spearman correlation does not assume that both datasets are normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact monotonic relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Spearman correlation at least as extreme as the one computed from these datasets. The p-values are not entirely reliable but are probably reasonable for datasets larger than 500 or so.
Parameters ---------- a, b : 1D or 2D array_like, b is optional One or two 1-D or 2-D arrays containing multiple variables and observations. When these are 1-D, each represents a vector of observations of a single variable. For the behavior in the 2-D case, see under ``axis``, below. Both arrays need to have the same length in the ``axis`` dimension. axis : int or None, optional If axis=0 (default), then each column represents a variable, with observations in the rows. If axis=1, the relationship is transposed: each row represents a variable, while the columns contain observations. If axis=None, then both arrays will be raveled. nan_policy : 'propagate', 'raise', 'omit', optional Defines how to handle when input contains nan. The following options are available (default is 'propagate'):
* 'propagate': returns nan * 'raise': throws an error * 'omit': performs the calculations ignoring nan values
Returns ------- correlation : float or ndarray (2-D square) Spearman correlation matrix or correlation coefficient (if only 2 variables are given as parameters. Correlation matrix is square with length equal to total number of variables (columns or rows) in ``a`` and ``b`` combined. pvalue : float The two-sided p-value for a hypothesis test whose null hypothesis is that two sets of data are uncorrelated, has same dimension as rho.
References ---------- .. 1 Zwillinger, D. and Kokoska, S. (2000). CRC Standard Probability and Statistics Tables and Formulae. Chapman & Hall: New York. 2000. Section 14.7
Examples -------- >>> from scipy import stats >>> stats.spearmanr(1,2,3,4,5, 5,6,7,8,7) (0.82078268166812329, 0.088587005313543798) >>> np.random.seed(1234321) >>> x2n = np.random.randn(100, 2) >>> y2n = np.random.randn(100, 2) >>> stats.spearmanr(x2n) (0.059969996999699973, 0.55338590803773591) >>> stats.spearmanr(x2n:,0, x2n:,1) (0.059969996999699973, 0.55338590803773591) >>> rho, pval = stats.spearmanr(x2n, y2n) >>> rho array([ 1. , 0.05997 , 0.18569457, 0.06258626],
[ 0.05997 , 1. , 0.110003 , 0.02534653],
[ 0.18569457, 0.110003 , 1. , 0.03488749],
[ 0.06258626, 0.02534653, 0.03488749, 1. ]) >>> pval array([ 0. , 0.55338591, 0.06435364, 0.53617935],
[ 0.55338591, 0. , 0.27592895, 0.80234077],
[ 0.06435364, 0.27592895, 0. , 0.73039992],
[ 0.53617935, 0.80234077, 0.73039992, 0. ]) >>> rho, pval = stats.spearmanr(x2n.T, y2n.T, axis=1) >>> rho array([ 1. , 0.05997 , 0.18569457, 0.06258626],
[ 0.05997 , 1. , 0.110003 , 0.02534653],
[ 0.18569457, 0.110003 , 1. , 0.03488749],
[ 0.06258626, 0.02534653, 0.03488749, 1. ]) >>> stats.spearmanr(x2n, y2n, axis=None) (0.10816770419260482, 0.1273562188027364) >>> stats.spearmanr(x2n.ravel(), y2n.ravel()) (0.10816770419260482, 0.1273562188027364)
>>> xint = np.random.randint(10, size=(100, 2)) >>> stats.spearmanr(xint) (0.052760927029710199, 0.60213045837062351)