def __create_variable_vardata(self, dataset: Dataset,
expr: Variable) -> VarData:
# dataframe = dataset[expr.name] # I don't know if we want this. We may want to just store query (in metadata?) and
# then use query to get raw data later....(for user, not interpreter?)
metadata = dataset.get_variable_data(expr.name) # (dtype, categories)
# if expr.name == 'strategy':
# import pdb; pdb.set_trace()
metadata['var_name'] = expr.name
metadata['query'] = ''
return VarData(metadata)
def evaluate(dataset: Dataset,
expr: Node,
assumptions: Dict[str, str],
design: Dict[str, str] = None):
if isinstance(expr, Variable):
# dataframe = dataset[expr.name] # I don't know if we want this. We may want to just store query (in metadata?) and
# then use query to get raw data later....(for user, not interpreter?)
metadata = dataset.get_variable_data(expr.name) # (dtype, categories)
# if expr.name == 'strategy':
# import pdb; pdb.set_trace()
metadata['var_name'] = expr.name
metadata['query'] = ''
return VarData(metadata)
elif isinstance(expr, Literal):
data = pd.Series(
[expr.value] * len(dataset.data),
index=dataset.data.index) # Series filled with literal value
# metadata = None # metadata=None means literal
metadata = dict() # metadata=None means literal
metadata['var_name'] = '' # because not a var in the dataset
metadata['query'] = ''
metadata['value'] = expr.value
return VarData(data, metadata)
elif isinstance(expr, Equal):
lhs = evaluate(dataset, expr.lhs)
rhs = evaluate(dataset, expr.rhs)
assert isinstance(lhs, VarData)
assert isinstance(rhs, VarData)
dataframe = lhs.dataframe[lhs.dataframe == rhs.dataframe]
metadata = lhs.metadata
if (isinstance(expr.rhs, Literal)):
metadata[
'query'] = f" == \'{rhs.metadata['value']}\'" # override lhs metadata for query
elif (isinstance(expr.rhs, Variable)):
metadata['query'] = f" == {rhs.metadata['var_name']}"
else:
raise ValueError(f"Not implemented for {rhs}")
return VarData(metadata)
elif isinstance(expr, NotEqual):
rhs = evaluate(dataset, expr.rhs)
lhs = evaluate(dataset, expr.lhs)
assert isinstance(rhs, VarData)
assert isinstance(lhs, VarData)
dataframe = lhs.dataframe[lhs.dataframe != rhs.dataframe]
metadata = lhs.metadata
if (isinstance(expr.rhs, Literal)):
metadata['query'] = " != \'\'" # override lhs metadata for query
elif (isinstance(expr.rhs, Variable)):
metadata['query'] = f" != {rhs.metadata['var_name']}"
else:
raise ValueError(f"Not implemented for {rhs}")
return VarData(metadata)
elif isinstance(expr, LessThan):
lhs = evaluate(dataset, expr.lhs)
rhs = evaluate(dataset, expr.rhs)
assert isinstance(lhs, VarData)
assert isinstance(rhs, VarData)
dataframe = None
metadata = rhs.metadata
if (not lhs.metadata):
raise ValueError(
'Malformed Relation. Filter on Variables must have variable as rhs'
)
elif (lhs.metadata['dtype'] is DataType.NOMINAL):
raise ValueError('Cannot compare nominal values with Less Than')
elif (lhs.metadata['dtype'] is DataType.ORDINAL):
# TODO May want to add a case should RHS and LHS both be variables
# assert (rhs.metadata is None)
comparison = rhs.dataframe.iloc[0]
if (isinstance(comparison, str)):
categories = lhs.metadata['categories'] # OrderedDict
# Get raw Pandas Series indices for desired data
ids = [
i for i, x in enumerate(lhs.dataframe)
if categories[x] < categories[comparison]
]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
elif (np.issubdtype(comparison, np.integer)):
categories = lhs.metadata['categories'] # OrderedDict
# Get raw Pandas Series indices for desired data
ids = [
i for i, x in enumerate(lhs.dataframe)
if categories[x] < comparison
]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
else:
raise ValueError(
f"Cannot compare ORDINAL variables to {type(rhs.dataframe.iloc[0])}"
)
elif (lhs.metadata['dtype'] is DataType.INTERVAL
or lhs.metadata['dtype'] is DataType.RATIO):
comparison = rhs.dataframe.iloc[0]
# Get raw Pandas Series indices for desired data
ids = [i for i, x in enumerate(lhs.dataframe) if x < comparison]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
else:
raise Exception(f"Invalid Less Than Operation:{lhs} < {rhs}")
if (isinstance(expr.rhs, Literal)):
metadata['query'] = " < \'\'" # override lhs metadata for query
elif (isinstance(expr.rhs, Variable)):
metadata['query'] = f" < {rhs.metadata['var_name']}"
else:
raise ValueError(f"Not implemented for {rhs}")
return VarData(metadata)
elif isinstance(expr, LessThanEqual):
lhs = evaluate(dataset, expr.lhs)
rhs = evaluate(dataset, expr.rhs)
assert isinstance(lhs, VarData)
assert isinstance(rhs, VarData)
dataframe = None
metadata = rhs.metadata
if (not lhs.metadata):
raise ValueError(
'Malformed Relation. Filter on Variables must have variable as rhs'
)
elif (lhs.metadata['dtype'] is DataType.NOMINAL):
raise ValueError('Cannot compare nominal values with Less Than')
elif (lhs.metadata['dtype'] is DataType.ORDINAL):
# TODO May want to add a case should RHS and LHS both be variables
# assert (rhs.metadata is None)
comparison = rhs.dataframe.iloc[0]
if (isinstance(comparison, str)):
categories = lhs.metadata['categories'] # OrderedDict
# Get raw Pandas Series indices for desired data
ids = [
i for i, x in enumerate(lhs.dataframe)
if categories[x] <= categories[comparison]
]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
elif (np.issubdtype(comparison, np.integer)):
categories = lhs.metadata['categories'] # OrderedDict
# Get raw Pandas Series indices for desired data
ids = [
i for i, x in enumerate(lhs.dataframe)
if categories[x] <= comparison
]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
else:
raise ValueError(
f"Cannot compare ORDINAL variables to {type(rhs.dataframe.iloc[0])}"
)
elif (lhs.metadata['dtype'] is DataType.INTERVAL
or lhs.metadata['dtype'] is DataType.RATIO):
comparison = rhs.dataframe.iloc[0]
# Get raw Pandas Series indices for desired data
ids = [i for i, x in enumerate(lhs.dataframe) if x <= comparison]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
else:
raise Exception(
f"Invalid Less Than Equal Operation:{lhs} <= {rhs}")
if (isinstance(expr.rhs, Literal)):
metadata['query'] = " <= \'\'" # override lhs metadata for query
elif (isinstance(expr.rhs, Variable)):
metadata['query'] = f" <= {rhs.metadata['var_name']}"
else:
raise ValueError(f"Not implemented for {rhs}")
return VarData(metadata)
elif isinstance(expr, GreaterThan):
lhs = evaluate(dataset, expr.lhs)
rhs = evaluate(dataset, expr.rhs)
assert isinstance(lhs, VarData)
assert isinstance(rhs, VarData)
dataframe = None
metadata = rhs.metadata
if (not lhs.metadata):
raise ValueError(
'Malformed Relation. Filter on Variables must have variable as rhs'
)
elif (lhs.metadata['dtype'] is DataType.NOMINAL):
raise ValueError('Cannot compare nominal values with Greater Than')
elif (lhs.metadata['dtype'] is DataType.ORDINAL):
# TODO May want to add a case should RHS and LHS both be variables
# assert (rhs.metadata is None)
comparison = rhs.dataframe.iloc[0]
if (isinstance(comparison, str)):
categories = lhs.metadata['categories'] # OrderedDict
# Get raw Pandas Series indices for desired data
ids = [
i for i, x in enumerate(lhs.dataframe)
if categories[x] > categories[comparison]
]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
elif (np.issubdtype(comparison, np.integer)):
categories = lhs.metadata['categories'] # OrderedDict
# Get raw Pandas Series indices for desired data
ids = [
i for i, x in enumerate(lhs.dataframe)
if categories[x] > comparison
]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
else:
raise ValueError(
f"Cannot compare ORDINAL variables to {type(rhs.dataframe.iloc[0])}"
)
elif (lhs.metadata['dtype'] is DataType.INTERVAL
or lhs.metadata['dtype'] is DataType.RATIO):
comparison = rhs.dataframe.iloc[0]
# Get raw Pandas Series indices for desired data
ids = [i for i, x in enumerate(lhs.dataframe) if x > comparison]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
else:
raise Exception(f"Invalid Greater Than Operation:{lhs} > {rhs}")
if (isinstance(expr.rhs, Literal)):
metadata['query'] = " > \'\'" # override lhs metadata for query
elif (isinstance(expr.rhs, Variable)):
metadata['query'] = f" > {rhs.metadata['var_name']}"
else:
raise ValueError(f"Not implemented for {rhs}")
return VarData(metadata)
elif isinstance(expr, GreaterThanEqual):
lhs = evaluate(dataset, expr.lhs)
rhs = evaluate(dataset, expr.rhs)
assert isinstance(lhs, VarData)
assert isinstance(rhs, VarData)
dataframe = None
metadata = rhs.metadata
if (not lhs.metadata):
raise ValueError(
'Malformed Relation. Filter on Variables must have variable as rhs'
)
elif (lhs.metadata['dtype'] is DataType.NOMINAL):
raise ValueError(
'Cannot compare nominal values with Greater Than Equal')
elif (lhs.metadata['dtype'] is DataType.ORDINAL):
# TODO May want to add a case should RHS and LHS both be variables
# assert (rhs.metadata is None)
comparison = rhs.dataframe.iloc[0]
if (isinstance(comparison, str)):
categories = lhs.metadata['categories'] # OrderedDict
# Get raw Pandas Series indices for desired data
ids = [
i for i, x in enumerate(lhs.dataframe)
if categories[x] >= categories[comparison]
]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
elif (np.issubdtype(comparison, np.integer)):
categories = lhs.metadata['categories'] # OrderedDict
# Get raw Pandas Series indices for desired data
ids = [
i for i, x in enumerate(lhs.dataframe)
if categories[x] >= comparison
]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
else:
raise ValueError(
f"Cannot compare ORDINAL variables to {type(rhs.dataframe.iloc[0])}"
)
elif (lhs.metadata['dtype'] is DataType.INTERVAL
or lhs.metadata['dtype'] is DataType.RATIO):
comparison = rhs.dataframe.iloc[0]
# Get raw Pandas Series indices for desired data
ids = [i for i, x in enumerate(lhs.dataframe) if x >= comparison]
# Get Pandas Series set indices for desired data
p_ids = [lhs.dataframe.index.values[i] for i in ids]
# Create new Pandas Series with only the desired data, using set indices
dataframe = pd.Series(lhs.dataframe, p_ids)
dataframe.index.name = dataset.pid_col_name
else:
raise Exception(
f"Invalid Greater Than Equal Operation:{lhs} >= {rhs}")
if (isinstance(expr.rhs, Literal)):
metadata['query'] = " >= \'\'" # override lhs metadata for query
elif (isinstance(expr.rhs, Variable)):
metadata['query'] = f" >= {rhs.metadata['var_name']}"
else:
raise ValueError(f"Not implemented for {rhs}")
return VarData(metadata)
elif isinstance(expr, Relate):
vars = []
for v in expr.vars:
eval_v = evaluate(dataset, v, design)
if not eval_v:
raise ValueError(
"The variables you are referencing are not defined as variables in your list of variables."
)
assert isinstance(eval_v, VarData)
vars.append(eval_v)
# What kind of study are we analyzing?
study_type = determine_study_type(vars, design)
# Assign roles to variables we are analyzing
vars = assign_roles(vars, study_type, design)
combined_data = None
# Do we have a Bivariate analysis?
if len(vars) == 2:
combined_data = BivariateData(vars,
study_type,
alpha=float(assumptions['alpha']))
else: # Do we have a Multivariate analysis?
combined_data = MultivariateData(vars,
study_type,
alpha=float(assumptions['alpha']))
# Add paired property
add_paired_property(dataset, combined_data, study_type,
design) # check sample sizes are identical
# Infer stats tests (mingled with)
tests = synthesize_tests(dataset, assumptions, combined_data)
""""
# verify_properties(properties_and_tests)
# get_tests
# execute_tests
# interpret_tests_results
# print(tests)
for test in tests:
print("\nValid test: %s" % test.name)
print("Properties:")
properties = test.properties()
for prop in properties:
property_identifier = ""
if prop.scope == "test":
property_identifier = test.name + ": " + prop.name
else:
for var_indices in test.properties_for_vars[prop]:
for var_index in var_indices:
property_identifier += f"variable {test.test_vars[var_index].name} "
property_identifier += ": %s" % prop.name
print(property_identifier)
"""
# Execute and store results from each valid test
results = {}
if len(tests) == 0:
tests.append('bootstrap') # Default to bootstrap
for test in tests:
test_result = execute_test(dataset, design, expr.predictions,
combined_data, test)
results[test] = test_result
res_data = ResultData(results, combined_data)
follow_up = []
# There are multiple hypotheses to follow-up and correct for
if expr.predictions and len(expr.predictions) > 1:
for pred in expr.predictions:
# create follow-up expr Node (to evaluate recursively)
pred_res = evaluate(dataset, pred, assumptions, design)
follow_up.append(pred_res) # add follow-up result to follow_up
res_data.add_follow_up(
follow_up) # add follow-up results to the res_data object
"""
# TODO: use a handle here to more generally/modularly support corrections, need a more generic data structure for this!
if expr.predictions:
preds = expr.predictions
# There are multiple comparisons
# if len(preds > 1):
# FOR DEBUGGING:
if len(preds) >= 1:
correct_multiple_comparison(res_data, len(preds))
"""
# import pdb; pdb.set_trace()
return res_data
elif isinstance(expr, PositiveRelationship):
# get variables
vars = [expr.lhs.var, expr.rhs.var]
# create a Relate object
pos_relate_expr = Relate(vars)
return evaluate(dataset, pos_relate_expr, assumptions, design)