def test_make_tpot_pmml_config(self):
config = {
"sklearn.kernel_approximation.RBFSampler" : {"gamma" : numpy.arange(0.0, 1.01, 0.05)},
"sklearn.preprocessing.StandardScaler" : {}
}
tpot_pmml_config = make_tpot_pmml_config(config)
self.assertEqual({"sklearn.preprocessing.StandardScaler" : {}}, tpot_pmml_config)
def build_classifier(data, feature_pipeline, generations, population_size,
name):
X, y = data
Xt = feature_pipeline.fit_transform(X)
Xt = Xt.astype(float)
categories = pandas.unique(y)
config = make_tpot_pmml_config(classifier_config_dict)
config = filter_config(config)
del config[
"sklearn.naive_bayes.GaussianNB"] # Does not support nesting - see http://mantis.dmg.org/view.php?id=208
del config["sklearn.neighbors.KNeighborsClassifier"]
del config[
"sklearn.svm.LinearSVC"] # Does not support classifier.predict_proba(Xt)
del config["sklearn.tree.DecisionTreeClassifier"]
classifier = TPOTClassifier(generations=generations,
population_size=population_size,
random_state=13,
config_dict=config,
verbosity=2)
classifier.fit(Xt, y)
pipeline = make_pmml_pipeline(Pipeline(steps=feature_pipeline.steps +
classifier.fitted_pipeline_.steps),
active_fields=X.columns.values,
target_fields=[y.name])
print(repr(pipeline))
store_pkl(pipeline, name)
result = DataFrame(classifier.predict(Xt), columns=[y.name])
if (len(categories) > 0):
probabilities = DataFrame(classifier.predict_proba(Xt),
columns=[
"probability(" + str(category) + ")"
for category in categories
])
result = pandas.concat([result, probabilities], axis=1)
store_csv(result, name)
def build_classifier(data, name):
X, y = data
categories = pandas.unique(y)
config = make_tpot_pmml_config(classifier_config_dict)
del config["sklearn.neighbors.KNeighborsClassifier"]
classifier = TPOTClassifier(generations=1,
population_size=3,
random_state=13,
config_dict=config,
verbosity=2)
classifier.fit(X, y)
pipeline = make_pmml_pipeline(classifier.fitted_pipeline_,
active_fields=X.columns.values,
target_fields=[y.name])
print(repr(pipeline))
store_pkl(pipeline, name + ".pkl")
result = DataFrame(classifier.predict(X), columns=[y.name])
if (len(categories) > 0):
probabilities = DataFrame(classifier.predict_proba(X),
columns=[
"probability(" + str(category) + ")"
for category in categories
])
result = pandas.concat([result, probabilities], axis=1)
store_csv(result, name + ".csv")
def build_regressor(data, name): X, y = data config = make_tpot_pmml_config(regressor_config_dict) del config["sklearn.neighbors.KNeighborsRegressor"] regressor = TPOTRegressor(generations = 3, population_size = 3, random_state = 13, config_dict = config, verbosity = 2) regressor.fit(X, y) pipeline = make_pmml_pipeline(regressor.fitted_pipeline_, active_fields = X.columns.values, target_fields = [y.name]) print(repr(pipeline)) store_pkl(pipeline, name) result = DataFrame(regressor.predict(X), columns = [y.name]) store_csv(result, name)
def build_regressor(data, feature_pipeline, generations, population_size,
name):
X, y = data
Xt = feature_pipeline.fit_transform(X)
Xt = Xt.astype(float)
config = make_tpot_pmml_config(regressor_config_dict)
config = filter_config(config)
del config["sklearn.neighbors.KNeighborsRegressor"]
regressor = TPOTRegressor(generations=generations,
population_size=population_size,
random_state=13,
config_dict=config,
verbosity=2)
regressor.fit(Xt, y)
pipeline = Pipeline(steps=feature_pipeline.steps +
regressor.fitted_pipeline_.steps)
pipeline = make_pmml_pipeline(pipeline,
active_fields=X.columns.values,
target_fields=[y.name])
print(repr(pipeline))
store_pkl(pipeline, name)
result = DataFrame(regressor.predict(Xt), columns=[y.name])
store_csv(result, name)