# This file is part of Sympathy for Data.
# Copyright (c) 2017, Combine Control Systems AB
#
# Sympathy for Data is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
# Sympathy for Data is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Sympathy for Data. If not, see <http://www.gnu.org/licenses/>.
import inspect
import numpy as np
import warnings
import sklearn
import sklearn.base
import sklearn.exceptions
import sklearn.feature_selection
import scipy.sparse
from sympathy.api import node
from sympathy.api.nodeconfig import Port, Ports, Tag, Tags
from sympathy.api.exceptions import SyDataError, sywarn
from sylib.machinelearning.model import ModelPort
from sylib.machinelearning.abstract_nodes import SyML_abstract
from sylib.machinelearning.descriptors import Descriptor
from sylib.machinelearning.descriptors import FloatType
from sylib.machinelearning.descriptors import NoneType
from sylib.machinelearning.descriptors import StringSelectionType
from sylib.machinelearning.descriptors import UnionType
from sylib.machinelearning.utility import data_to_table
from sylib.machinelearning.utility import table_to_array
from sylib.machinelearning.utility import array_to_table
[docs]class Fit(node.Node):
name = 'Fit'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'fit.svg'
description = (
'Trains a model. Use "Create Input Port > Y" for supervised training')
nodeid = 'org.sysess.sympathy.machinelearning.fit'
tags = Tags(Tag.MachineLearning.Apply)
inputs = Ports([ModelPort('Input model', 'in-model'),
Port.Table('X', name='X'),
Port.Custom('table', 'Y', name='Y0', n=(0, 1, 1)),
Port.Custom('table', 'sample_weights',
name='sample_weights', n=(0, 1))])
outputs = Ports([ModelPort('Output model', 'out-model')])
def execute(self, node_context):
X_tbl = node_context.input['X']
Y_tbls = node_context.input.group('Y0')
in_model = node_context.input['in-model']
out_model = node_context.output['out-model']
sample_weights = node_context.input.group('sample_weights')
if len(Y_tbls) > 0:
Y_tbl = Y_tbls[0]
Y = table_to_array(Y_tbls[0], unitary=True)
else:
Y = None
Y_tbl = None
if len(sample_weights) > 0:
sample_weight = table_to_array(sample_weights[0], unitary=True)
else:
sample_weight = None
out_model.source(in_model)
out_model.load()
model = out_model.get_skl()
X = table_to_array(X_tbl)
# Check if we can fit in a progress_update function
kwargs = {}
if 'progress_fn' in inspect.signature(model.fit).parameters:
kwargs['progress_fn'] = lambda i: self.set_progress(i)
with warnings.catch_warnings():
warnings.simplefilter(
'ignore', sklearn.exceptions.ConvergenceWarning)
if Y is None:
if sample_weight is None:
model.fit(X, **kwargs)
else:
model.fit(X, sample_weight=sample_weight, **kwargs)
else:
if sample_weight is None:
model.fit(X, Y, **kwargs)
else:
model.fit(X, Y, sample_weight=sample_weight, **kwargs)
desc = out_model.get_desc()
desc.set_x_names(X_tbl.column_names())
if Y_tbl is not None:
desc.set_y_names(Y_tbl.column_names())
desc.post_fit(model)
out_model.save()
[docs]class FitText(node.Node):
name = 'Fit Texts'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'fit_text.svg'
description = (
'Fits a model using lists of texts. '
'Use "Create Input Port > Y" for supervised training')
nodeid = 'org.sysess.sympathy.machinelearning.fit_text'
tags = Tags(Tag.MachineLearning.Apply)
inputs = Ports([ModelPort('Input model', 'in-model'),
Port.Custom('[text]', 'X', name='X'),
Port.Custom('table', 'Y', name='Y0', n=(0, 1, 1))])
outputs = Ports([ModelPort('Output model', 'out-model')])
def execute(self, node_context):
Y_tbls = node_context.input.group('Y0')
in_model = node_context.input['in-model']
out_model = node_context.output['out-model']
X = [x.get() for x in node_context.input['X']]
if len(Y_tbls) > 0:
Y_tbl = Y_tbls[0]
else:
Y_tbl = None
out_model.source(in_model)
out_model.load()
model = out_model.get_skl()
with warnings.catch_warnings():
warnings.simplefilter(
'ignore', sklearn.exceptions.ConvergenceWarning)
if Y_tbl is None:
model.fit(X)
else:
Y = table_to_array(Y_tbl, unitary=True)
model.fit(X, Y)
desc = out_model.get_desc()
desc.set_x_names(['corpus'])
if Y_tbl is not None:
desc.set_y_names(Y_tbl.column_names())
desc.post_fit(model)
out_model.save()
[docs]class Predict(node.Node):
name = 'Predict'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'predict.svg'
description = 'Uses a model to predict Y given X'
nodeid = 'org.sysess.sympathy.machinelearning.predict'
tags = Tags(Tag.MachineLearning.Apply)
parameters = node.parameters()
parameters.set_boolean(
'pass_x', label='Pass through X', value=False,
description=(
'Passes through a copy of X in addition to the predicted values'))
inputs = Ports([ModelPort('Input model', 'in-model'),
Port.Table('X', name='X')])
outputs = Ports([Port.Table('Y', name='Y')])
def execute(self, node_context):
X_tbl = node_context.input['X']
Y_tbl = node_context.output['Y']
in_model = node_context.input['in-model']
pass_x = node_context.parameters['pass_x'].value
in_model.load()
model = in_model.get_skl()
desc = in_model.get_desc()
X = table_to_array(X_tbl)
try:
Y = desc.predict(model, X)
except TypeError:
raise SyDataError(
"Model does not implement the 'predict' function")
return
if pass_x:
for col in X_tbl.cols():
Y_tbl.set_column_from_array(col.name, col.data)
if len(Y.shape) < 2:
Y = Y.reshape(Y.shape + (1,))
y_names = in_model.get_desc().y_names
if y_names is None:
y_names = ["y{0}".format(i) for i in range(Y.shape[1])]
for i, name in enumerate(y_names):
Y_tbl.set_column_from_array(name, Y[:, i])
[docs]class PredictProbabilities(node.Node):
name = 'Predict Probabilities'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'probabilities.svg'
description = (
'Uses a model to predict Y given X and returns the estimated'
'probabilities for each class in Y')
nodeid = 'org.sysess.sympathy.machinelearning.predict_proba'
tags = Tags(Tag.MachineLearning.Apply)
inputs = Ports([ModelPort('Input model', 'in-model'),
Port.Table('X', name='X')])
outputs = Ports([Port.Table('Y', name='Y')])
parameters = node.parameters()
parameters.set_string(
'names method',
label='Output names',
value='From classes',
description='Method used to generate output names',
editor=node.editors.combo_editor(options=[
'From classes', 'By index', 'From model Y names']))
def execute(self, node_context):
X_tbl = node_context.input['X']
Y_tbl = node_context.output['Y']
in_model = node_context.input['in-model']
names = node_context.parameters['names method'].value
in_model.load()
model = in_model.get_skl()
X = table_to_array(X_tbl)
try:
Y = model.predict_proba(X)
except TypeError:
raise SyDataError(
"Model does not implement the 'predict' function")
return
if type(Y) is list:
Y = np.concatenate(Y, axis=1)
if len(Y.shape) < 2:
Y = Y.reshape(Y.shape+(1,))
y_names = ["Y{0}".format(i) for i in range(Y.shape[1])]
if names == 'From classes':
try:
y_names = [str(classname) for classname in model.classes_]
except AttributeError:
pass
elif names == 'From model Y names':
cols = in_model.get_desc().y_names
y_names[:len(cols)] = cols
for i, name in enumerate(y_names):
Y_tbl.set_column_from_array(name, Y[:, i])
[docs]class DecisionFunction(node.Node):
name = 'Decision Function'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'decision_function.svg'
description = (
'Applies the decision function (if available) of a trained model '
'to return a scalar for each class of outputs')
nodeid = 'org.sysess.sympathy.machinelearning.decision_function'
tags = Tags(Tag.MachineLearning.Apply)
inputs = Ports([ModelPort('Input model', 'in-model'),
Port.Table('X', name='X')])
outputs = Ports([Port.Table('Y', name='Y')])
def execute(self, node_context):
X_tbl = node_context.input['X']
Y_tbl = node_context.output['Y']
in_model = node_context.input['in-model']
in_model.load()
model = in_model.get_skl()
X = table_to_array(X_tbl)
try:
Y = model.decision_function(X)
except TypeError:
raise SyDataError("Model does not implement 'decision_function'")
return
if len(Y.shape) < 2:
Y = Y.reshape(Y.shape + (1,))
y_names = in_model.get_desc().y_names
if y_names is not None and len(y_names) < Y.shape[1]:
y_names = None
if y_names is None:
try:
y_names = model.classes_
except AttributeError:
# Well, we tried - use fallback names
pass
if y_names is None:
y_names = ["y{0}".format(i) for i in range(Y.shape[1])]
for i, name in enumerate(y_names):
Y_tbl.set_column_from_array(name, Y[:, i])
[docs]class FitTransformText(node.Node):
name = 'Fit Transform Text'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'transform_text.svg'
description = (
'Fits a transform model to the given text data and computes '
'the transformed data. '
'Use "Create Input Port > Y" for supervised training')
nodeid = 'org.sysess.sympathy.machinelearning.fit_transform_text'
tags = Tags(Tag.MachineLearning.Apply)
inputs = Ports([ModelPort('Input model', 'in-model'),
Port.Custom('[text]', 'X', name='X')])
outputs = Ports([ModelPort('Output model', 'out-model'),
Port.Table('Output table', name='output')])
parameters = node.parameters()
parameters.set_string(
'names method', label='Output names', value='From model',
description='Method used to generate output names',
editor=node.editors.combo_editor(options=[
'By index', 'From model']))
parameters.set_boolean(
'transpose', value=False, label='Transpose output',
description='Transposes output data, suitable for large '
'number of features (eg. word counts)')
def execute(self, node_context):
out_tbl = node_context.output['output']
in_model = node_context.input['in-model']
out_model = node_context.output['out-model']
names = node_context.parameters['names method'].value
transpose = node_context.parameters['transpose'].value
X = [x.get() for x in node_context.input['X']]
out_model.source(in_model)
out_model.load()
transform = out_model.get_skl()
try:
Xprim = transform.fit_transform(X)
except TypeError:
raise SyDataError(
'Model does not implement transforms with only one input')
return
desc = out_model.get_desc()
desc.post_fit(transform)
out_model.save()
if names == 'From model' and desc.y_names is not None:
cols = desc.xout_names
else:
cols = []
Xprim = data_to_table(Xprim, cols, out_tbl, transpose=transpose)
[docs]class TransformText(node.Node):
name = 'Transform Text'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'transform_text.svg'
description = 'Applies a transformation model to the given text data'
nodeid = 'org.sysess.sympathy.machinelearning.transform_text'
tags = Tags(Tag.MachineLearning.Apply)
inputs = Ports([ModelPort('Input model', 'in-model'),
Port.Custom('[text]', 'X', name='X')])
outputs = Ports([Port.Table('Output table', name='output')])
parameters = node.parameters()
parameters.set_string(
'names method', label='Output names', value='From model',
description='Method used to generate output names',
editor=node.editors.combo_editor(options=[
'By index', 'From model']))
parameters.set_boolean(
'transpose', value=False, label='Transpose output',
description='Transposes output data, suitable for large '
'number of features (eg. word counts)')
def execute(self, node_context):
out_tbl = node_context.output['output']
in_model = node_context.input['in-model']
names = node_context.parameters['names method'].value
transpose = node_context.parameters['transpose'].value
X = [x.get() for x in node_context.input['X']]
in_model.load()
transform = in_model.get_skl()
desc = in_model.get_desc()
try:
Xprim = transform.transform(X)
except TypeError:
raise SyDataError(
'Given model does not implement transforms (one input)')
return
except sklearn.exceptions.NotFittedError as e:
raise SyDataError(repr(e))
if scipy.sparse.issparse(Xprim):
Xprim = Xprim.toarray()
if len(Xprim.shape) < 2:
Xprim = Xprim.reshape(Xprim.shape+(1,))
if names == 'From model' and desc.y_names is not None:
cols = desc.y_names
else:
cols = []
Xprim = data_to_table(Xprim, cols, out_tbl, transpose=transpose)
[docs]class Score(node.Node):
name = 'Score'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'score.svg'
description = (
'Scores the model using given X and Y data. Exact semantics\n'
'depends on the type of model (classifier, regressor, etc).')
nodeid = 'org.sysess.sympathy.machinelearning.score'
tags = Tags(Tag.MachineLearning.Metrics)
parameters = node.parameters()
parameters.set_boolean(
'default method', label='Use built-in default scoring',
value=True,
description=(
'Uses the default scoring method defined by the used model.\n'
'Semantics of the scoring depend on the type of node\n\n'
'(classifier, regressor, etc). Otherwise the problem is assumed\n'
'to be a classification problem, a single predict call is\n'
'made and extended information is given for each target. \n'
'If model does not implement the predict function then a\n'
'transform is used instead.'))
inputs = Ports([ModelPort('Input model', 'in-model'),
Port.Table('X', name='X'),
Port.Table('Y', name='Y')])
outputs = Ports([Port.Table('Score', name='Score')])
def execute(self, node_context):
X_tbl = node_context.input['X']
Y_tbl = node_context.input['Y']
in_model = node_context.input['in-model']
score_tbl = node_context.output['Score']
default_method = node_context.parameters['default method'].value
in_model.load()
model = in_model.get_skl()
desc = in_model.get_desc()
X = table_to_array(X_tbl)
Y = table_to_array(Y_tbl)
if list(desc.y_names) != list(Y_tbl.column_names()):
sywarn('Column names for Y does not match those in model')
if default_method:
try:
score = model.score(X, Y)
except TypeError:
raise SyDataError(
'Given model does not implement the "score" function')
else:
score_tbl.set_column_from_array('score', np.array([score]))
else:
try:
Y_pred = model.predict(X)
except TypeError:
try:
Y_pred = model.transform(X)
except TypeError:
raise SyDataError(
'Given model does not implement neither predict '
'nor transform')
if len(Y_pred.shape) == 1:
Y_pred = Y_pred.reshape(Y_pred.shape+(1,))
if len(Y.shape) == 1:
Y = Y.reshape(Y.shape+(1,))
if Y.shape != Y_pred.shape:
raise SyDataError(
'Shape of predicted Y-data {} does not match actual Y {}'
.format(Y_pred.shape, Y.shape))
correct = Y == Y_pred
score = np.all(correct, axis=1).mean()
score_tbl.set_column_from_array('score', np.array([score]))
if len(desc.y_names) > 1:
for pos, name in enumerate(desc.y_names):
col = correct[:, pos]
score_tbl.set_column_from_array(
name, np.array([col.mean()]))
[docs]class SelectFromModel(SyML_abstract, node.Node):
name = 'Select Features from Model'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'select_model.svg'
description = (
'Meta-transformer for selecting features based on importance weight. '
'Only works for models with coef or feature_importances attributes.'
)
nodeid = 'org.sysess.sympathy.machinelearning.select_from_model'
tags = Tags(Tag.MachineLearning.Apply)
descriptor = Descriptor()
descriptor.name = name
descriptor.set_info([
{'name': 'threshold',
'type': UnionType([
StringSelectionType(['median', 'mean']),
FloatType(), NoneType()], default=None)},
], doc_class=sklearn.feature_selection.SelectFromModel)
parameters = node.parameters()
SyML_abstract.generate_parameters(parameters, descriptor)
inputs = Ports([ModelPort('Model', 'model'),
Port.Table('in-data', name='in-data')])
outputs = Ports([Port.Table('out-data', name='out-data'),
Port.Table('features', name='features')])
__doc__ = SyML_abstract.generate_docstring(
description, descriptor.info, descriptor.attributes, inputs, outputs)
def execute(self, node_context):
model = node_context.input['model']
in_data = node_context.input['in-data']
out_data = node_context.output['out-data']
features = node_context.output['features']
model.load()
skl = model.get_skl()
desc = model.get_desc()
kwargs = self.__class__.descriptor.get_parameters(
node_context.parameters)
kwargs['estimator'] = skl
kwargs['prefit'] = True
sfm = sklearn.feature_selection.SelectFromModel(**kwargs)
X = table_to_array(in_data)
Xsel = sfm.transform(X)
indices = sfm.get_support(indices=True)
x_names = desc.x_names
if x_names is None:
x_names = ["X{}".format(i) for i in X.shape[1]]
array_to_table(np.array(x_names)[indices],
Xsel,
tbl=out_data)
support = sfm.get_support()
array_to_table(x_names,
support.reshape((1, len(support))),
tbl=features)
