def fit(self, X, y):
"""Run the search algorithm to synthesize a push program.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
y : list, array-like, or pandas dataframe.
The target values (class labels in classification, real numbers in
regression). Shape = [n_samples] or [n_samples, n_outputs]
"""
# arity is the number of inputs that the program takes.
X, y, arity, y_types = check_X_y(X, y)
output_types = [
self.interpreter.type_library.push_type_for_type(t).name
for t in y_types
]
if self.last_str_from_stdout:
ndx = list_rindex(output_types, "str")
if ndx is not None:
output_types[ndx] = "stdout"
self.signature = ProgramSignature(arity=arity,
output_stacks=output_types,
push_config=self.push_config)
self.evaluator = DatasetEvaluator(X, y, interpreter=self.interpreter)
self._build_search_algo()
self.solution = self.search.run()
self.search.config.tear_down()
def evaluate(self, program: CodeBlock) -> np.ndarray:
"""Evaluate the program and return the error vector.
Parameters
----------
program
Program (CodeBlock of Push code) to evaluate.
Returns
-------
np.ndarray
The error vector of the program.
"""
errors_on_cases = []
for ndx, case in enumerate(self.X):
expected = self.y[ndx]
if not isinstance(expected, (list, np.ndarray)):
expected = [expected]
output_types = [self.interpreter.type_library.push_type_of(_).name for _ in expected]
if self.last_str_from_stdout:
ndx = list_rindex(output_types, "str")
if ndx is not None:
output_types[ndx] = "stdout"
actual = self.interpreter.run(program, case, output_types)
errors_on_cases.append(self.default_error_function(actual, expected))
return np.array(errors_on_cases).flatten()
def fit(self, X, y):
"""Run the search algorithm to synthesize a push program.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
y : list, array-like, or pandas dataframe.
The target values (class labels in classification, real numbers in
regression). Shape = [n_samples] or [n_samples, n_outputs]
"""
X, y, arity, y_types = check_X_y(X, y)
self.interpreter.instruction_set.register_n_inputs(arity)
output_types = [push_type_for_type(t).name for t in y_types]
if self.last_str_from_stdout:
ndx = list_rindex(output_types, "str")
if ndx is not None:
output_types[ndx] = "stdout"
self.evaluator = DatasetEvaluator(
X,
y,
interpreter=self.interpreter,
last_str_from_stdout=self.last_str_from_stdout,
verbosity_config=DEFAULT_VERBOSITY_LEVELS[self.verbose])
self._build_search_algo()
best_seen = self.search.run()
self._result = SearchResult(best_seen.program, output_types)
def fit(self, X, y):
"""Run the search algorithm to synthesize a push program.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
y : list, array-like, or pandas dataframe.
The target values (class labels in classification, real numbers in
regression). Shape = [n_samples] or [n_samples, n_outputs]
"""
X, y, arity, y_types = check_X_y(X, y)
self.interpreter.instruction_set.register_n_inputs(arity)
output_types = [self.interpreter.type_library.push_type_for_type(t).name for t in y_types]
if self.last_str_from_stdout:
ndx = list_rindex(output_types, "str")
if ndx is not None:
output_types[ndx] = "stdout"
self.evaluator = DatasetEvaluator(
X, y,
interpreter=self.interpreter,
last_str_from_stdout=self.last_str_from_stdout
)
self._build_search_algo()
best_seen = self.search.run()
self._result = SearchResult(best_seen.program, output_types)
def evaluate(self, program: CodeBlock) -> np.ndarray:
"""Evaluate the program and return the error vector.
Parameters
----------
program
Program (CodeBlock of Push code) to evaluate.
Returns
-------
np.ndarray
The error vector of the program.
"""
errors_on_cases = []
for ndx, case in enumerate(self.X):
expected = self.y[ndx]
if not isinstance(expected, (list, np.ndarray)):
expected = [expected]
output_types = [push_type_of(_).name for _ in expected]
if self.last_str_from_stdout:
ndx = list_rindex(output_types, "str")
if ndx is not None:
output_types[ndx] = "stdout"
actual = self.interpreter.run(program, case, output_types,
self.verbosity_config)
errors_on_cases.append(
self.default_error_function(actual, expected))
return np.array(errors_on_cases).flatten()