def predict(self, input_data, full=False):
"""Returns the prediction and the confidence intervals
input_data: Input data to be predicted
full: Boolean that controls whether to include the prediction's
attributes. By default, only the prediction is produced. If set
to True, the rest of available information is added in a
dictionary format. The dictionary keys can be:
- prediction: the prediction value
- unused_fields: list of fields in the input data that
are not being used in the model
"""
# Checks and cleans input_data leaving the fields used in the model
unused_fields = []
norm_input_data = self.filter_input_data( \
input_data,
add_unused_fields=full)
if full:
norm_input_data, unused_fields = norm_input_data
# Strips affixes for numeric values and casts to the final field type
cast(norm_input_data, self.fields)
# In case that the training data has no missings, input data shouldn't
check_no_training_missings(norm_input_data, self.model_fields,
self.weight_field,
self.objective_id)
# Computes text and categorical field expansion
unique_terms = self.get_unique_terms(norm_input_data)
# Creates an input vector with the values for all expanded fields.
input_array = self.expand_input(norm_input_data, unique_terms)
compact_input_array = self.expand_input(norm_input_data, unique_terms,
True)
prediction = dot([flatten(self.coefficients)], [input_array])[0][0]
result = {
"prediction": prediction}
if self.xtx_inverse:
result.update({"confidence_bounds": self.confidence_bounds( \
compact_input_array)})
if full:
result.update({"unused_fields": unused_fields})
else:
result = result["prediction"]
return result
def predict(self, input_data, full=False):
"""Returns the prediction and the confidence intervals
input_data: Input data to be predicted
full: Boolean that controls whether to include the prediction's
attributes. By default, only the prediction is produced. If set
to True, the rest of available information is added in a
dictionary format. The dictionary keys can be:
- prediction: the prediction value
- unused_fields: list of fields in the input data that
are not being used in the model
"""
# Checks and cleans input_data leaving the fields used in the model
unused_fields = []
new_data = self.filter_input_data( \
input_data,
add_unused_fields=full)
if full:
new_data, unused_fields = new_data
# Strips affixes for numeric values and casts to the final field type
cast(new_data, self.fields)
# In case that the training data has no missings, input data shouldn't
check_no_training_missings(new_data, self.fields, self.weight_field,
self.objective_id)
# Computes text and categorical field expansion
unique_terms = self.get_unique_terms(new_data)
# Creates an input vector with the values for all expanded fields.
input_array = self.expand_input(new_data, unique_terms)
compact_input_array = self.expand_input(new_data, unique_terms, True)
prediction = dot([flatten(self.coefficients)], [input_array])[0][0]
result = {
"prediction": prediction}
if self.xtx_inverse is not None:
result.update({"confidence_bounds": self.confidence_bounds( \
compact_input_array)})
if full:
result.update({"unused_fields": unused_fields})
else:
result = result["prediction"]
return result
def categorical_encoding(self, inputs, field_id, compact):
"""Returns the result of combining the encoded categories
according to the field_codings projections
The result is the components generated by the categorical field
"""
new_inputs = inputs[:]
projections = self.field_codings[field_id].get( \
CONTRAST, self.field_codings[field_id].get(OTHER))
if projections is not None:
new_inputs = flatten(dot(projections, [new_inputs]))
if compact and self.field_codings[field_id].get(DUMMY) is not None:
dummy_class = self.field_codings[field_id][DUMMY]
index = self.categories[field_id].index(dummy_class)
cat_new_inputs = new_inputs[0:index]
if len(new_inputs) > (index + 1):
cat_new_inputs.extend(new_inputs[index + 1:])
new_inputs = cat_new_inputs
return new_inputs
def categorical_encoding(self, inputs, field_id, compact):
"""Returns the result of combining the encoded categories
according to the field_codings projections
The result is the components generated by the categorical field
"""
new_inputs = inputs[:]
projections = self.field_codings[field_id].get( \
CONTRAST, self.field_codings[field_id].get(OTHER))
if projections is not None:
new_inputs = flatten(dot(projections, [new_inputs]))
if compact and self.field_codings[field_id].get(DUMMY) is not None:
dummy_class = self.field_codings[field_id][DUMMY]
index = self.categories[field_id].index(dummy_class)
cat_new_inputs = new_inputs[0: index]
if len(new_inputs) > (index + 1):
cat_new_inputs.extend(new_inputs[index + 1 :])
new_inputs = cat_new_inputs
return new_inputs
