reconnect moved files to git repo

venv/lib/python3.11/site-packages/pmdarima/arima/_validation.py

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+# -*- coding: utf-8 -*-
+
+"""
+Arg validation for auto-arima calls. This allows us to test validation more
+directly without having to fit numerous combinations of models.
+"""
+
+import numpy as np
+import warnings
+from sklearn import metrics
+
+from pmdarima.warnings import ModelFitWarning
+
+# The valid information criteria
+VALID_CRITERIA = {'aic', 'aicc', 'bic', 'hqic', 'oob'}
+
+
+def auto_intercept(with_intercept, default):
+    """A more concise way to handle the default behavior of with_intercept"""
+    if with_intercept == "auto":
+        return default
+    return with_intercept
+
+
+def check_information_criterion(information_criterion, out_of_sample_size):
+    """Check whether the information criterion is valid"""
+    if information_criterion not in VALID_CRITERIA:
+        raise ValueError('auto_arima not defined for information_criteria=%s. '
+                         'Valid information criteria include: %r'
+                         % (information_criterion, VALID_CRITERIA))
+
+    # check on information criterion and out_of_sample size
+    if information_criterion == 'oob' and out_of_sample_size == 0:
+        information_criterion = 'aic'
+        warnings.warn('information_criterion cannot be \'oob\' with '
+                      'out_of_sample_size = 0. '
+                      'Falling back to information criterion = aic.')
+
+    return information_criterion
+
+
+def check_kwargs(kwargs):
+    """Return kwargs or an empty dict.
+
+    We often pass named kwargs (like `sarimax_kwargs`) as None by default. This
+    is to avoid a mutable default, which can bite you in unexpected ways. This
+    will return a kwarg-compatible value.
+    """
+    if kwargs:
+        return kwargs
+    return {}
+
+
+def check_m(m, seasonal):
+    """Check the value of M (seasonal periodicity)"""
+    if (m < 1 and seasonal) or m < 0:
+        raise ValueError('m must be a positive integer (> 0)')
+
+    if not seasonal:
+        # default m is 1, so if it's the default, don't warn
+        if m > 1:
+            warnings.warn("m (%i) set for non-seasonal fit. Setting to 0" % m)
+        m = 0
+
+    return m
+
+
+def check_n_jobs(stepwise, n_jobs):
+    """Potentially update the n_jobs parameter
+
+    We can't run in parallel with the stepwise algorithm. This checks
+    ``n_jobs`` w.r.t. stepwise and will warn.
+    """
+    if stepwise and n_jobs != 1:
+        n_jobs = 1
+        warnings.warn('stepwise model cannot be fit in parallel (n_jobs=%i). '
+                      'Falling back to stepwise parameter search.' % n_jobs)
+    return n_jobs
+
+
+def check_start_max_values(st, mx, argname):
+    """Ensure starting points and ending points are valid"""
+    if mx is None:
+        mx = np.inf
+    if st is None:
+        raise ValueError("start_%s cannot be None" % argname)
+    if st < 0:
+        raise ValueError("start_%s must be positive" % argname)
+    if mx < st:
+        raise ValueError("max_%s must be >= start_%s" % (argname, argname))
+    return st, mx
+
+
+def check_trace(trace):
+    """Check the value of trace"""
+    if trace is None:
+        return 0
+    if isinstance(trace, (int, bool)):
+        return int(trace)
+    # otherwise just be truthy with it
+    if trace:
+        return 1
+    return 0
+
+
+def get_scoring_metric(metric):
+    """Get a scoring metric by name, or passthrough a callable
+
+    Parameters
+    ----------
+    metric : str or callable
+        A name of a scoring metric, or a custom callable function. If it is a
+        callable, it must adhere to the signature::
+
+            def func(y_true, y_pred)
+
+        Note that the ARIMA model selection seeks to MINIMIZE the score, and it
+        is up to the user to ensure that scoring methods that return maximizing
+        criteria (i.e., ``r2_score``) are wrapped in a function that will
+        return the negative value of the score.
+    """
+    if isinstance(metric, str):
+
+        # XXX: legacy support, remap mse/mae to their long versions
+        if metric == "mse":
+            return metrics.mean_squared_error
+        if metric == "mae":
+            return metrics.mean_absolute_error
+
+        try:
+            return getattr(metrics, metric)
+        except AttributeError:
+            raise ValueError("'%s' is not a valid scoring method." % metric)
+
+    if not callable(metric):
+        raise TypeError("`metric` must be a valid scoring method, or a "
+                        "callable, but got type=%s" % type(metric))
+
+    # TODO: warn for potentially invalid signature?
+    return metric
+
+
+def warn_for_D(d, D):
+    """Warn for large values of D"""
+    if D >= 2:
+        warnings.warn("Having more than one seasonal differences is "
+                      "not recommended. Please consider using only one "
+                      "seasonal difference.", ModelFitWarning)
+    # if D is -1, this will be off, so we include the OR
+    # TODO: @FutureTayTay.. how can D be -1?
+    elif D + d > 2 or d > 2:
+        warnings.warn("Having 3 or more differencing operations is not "
+                      "recommended. Please consider reducing the total "
+                      "number of differences.", ModelFitWarning)