from collections import namedtuple
[docs]class Analysis:
"""The Analysis class holds information about an Analysis in
Stat-Ease 360. Instances of this class are typically created by
:func:`statease.client.SEClient.get_analysis`.
"""
def __init__(self, client, name):
self.client = client
self.name = name
def __str__(self):
return self.name
def __repr__(self):
return self.__str__()
[docs] def predict(self, points, coded=False):
""" Makes predictions at one or more points."""
result = self.client.send_payload({
"method": "POST",
"uri": "analysis/" + self.name + "/predict",
"points": points,
"coded": coded,
})
return result['payload']
[docs] def set_model(self, model):
""" Sets the model used in the analysis. """
self.client.send_payload({
"method": "POST",
"uri": "analysis/" + self.name + "/model",
"model": model,
})
[docs] def auto_select(self, initial_model, criterion, method, alpha=None, select_by_degree=False, **kwargs):
""" Performs an auto-selection on an initial model.
:param str initial_model: A model to auto-select a subset of terms from.
:param str criterion: The criterion used to evaluate each sub-model.
Valid options are "AICc", "BIC", "pValues",
or "AdjRSquared" (case-insensitive).
:param str method: The method used to select sub-models. Valid options
are "Forward", "Backward", "Stepwise", and
"AllHierarchical" (case-insensitive).
:param float alpha: This is the alpha used to either include or exclude
terms when using the "pValues" criterion. You can
also pass `alpha_in` and/or `alpha_out` to set
those values separately (setting both only has an
effect when using the "Stepwise" method).
:param bool select_by_degree: Restricts the sub-model comparisons to
lower order terms before considering
higher order terms.
:Example:
>>> result_model = analysis.auto_select('A+B+C+AB+BC+ABC', 'BICc', 'Backward')
>>> print(result_model)
>>> "A+B+AB"
"""
payload = {
"method": "POST",
"uri": "analysis/" + self.name + "/autoselect",
"model": initial_model,
"criterion": criterion,
"select_method": method,
"select_by_degree": select_by_degree,
}
if kwargs.get("alpha_in", alpha):
payload["alpha_in"] = kwargs.get("alpha_in", alpha)
if kwargs.get("alpha_out", alpha):
payload["alpha_out"] = kwargs.get("alpha_out", alpha)
result = self.client.send_payload(payload)
return result['payload']
[docs] def go_to_node(self, node):
""" Forces the Stat-Ease 360 GUI to display a particular node. """
self.client.send_payload({
"method": "POST",
"uri": "nodes",
"node": str(node),
"analysis": self.name,
})
[docs] def analyze(self):
""" Runs the analyses on the selected model. """
return self.get_anova()
[docs] def get_anova(self):
""" Retrieves an AnovaResults object for this analysis. """
return AnovaResults(self.client, self.name)
[docs] def get_diagnostics(self):
""" Retrieves an DiagnosticsResults object for this analysis. """
return DiagnosticsResults(self.client, self.name)
class AnovaResults:
def __init__(self, client, analysis_name):
self.client = client
self.analysis_name = analysis_name
result = self.client.send_payload({
"method": "GET",
"uri": "analysis/" + self.analysis_name + "/anova",
})
self.terms = []
for k, v in result['payload'].items():
if k == 'terms':
for term_dict in v:
term = namedtuple('Term', iter(term_dict.keys()))(**term_dict)
self.terms.append(term)
else:
setattr(self, k, v)
def __str__(self):
return """R2: {r2}
Adj R2: {adjr2}
Pred R2: {predr2}
PRESS: {press}
BIC: {bic}
AICc: {aicc}
Terms: {terms}""".format(
r2=self.r2,
adjr2=self.adj_r2,
predr2=self.pred_r2,
press=self.press,
bic=self.bic,
aicc=self.aicc,
terms=self.terms,
)
class DiagnosticsResults:
def __init__(self, client, analysis_name):
self.client = client
self.analysis_name = analysis_name
result = self.client.send_payload({
"method": "GET",
"uri": "analysis/" + self.analysis_name + "/diagnostics",
})
for k, v in result['payload'].items():
setattr(self, k, v)
def __str__(self):
return """Actual: {actual}
Predicted: {predicted}
Residuals: {residuals}
""".format(
actual=self.actual,
predicted=self.predicted,
residuals=self.residuals,
)
