def build_auto(regressor, name, **pmml_options):
cylinders_origin_mapping = {
(8, 1) : "8/1",
(6, 1) : "6/1",
(4, 1) : "4/1",
(6, 2) : "6/2",
(4, 2) : "4/2",
(4, 3) : "4/3"
}
mapper = DataFrameMapper([
(["cylinders", "origin"], [MultiDomain([CategoricalDomain(), CategoricalDomain()]), MultiLookupTransformer(cylinders_origin_mapping, default_value = "other"), LabelBinarizer()]),
(["cylinders"], Alias(ExpressionTransformer("X[0] % 2.0 > 0.0"), name = "odd(cylinders)", prefit = True)),
(["model_year"], [CategoricalDomain(), Binarizer(threshold = 77)], {"alias" : "bin(model_year, 77)"}), # Pre/post 1973 oil crisis effects
(["model_year", "origin"], [MultiDomain([CategoricalDomain(), CategoricalDomain()]), ConcatTransformer("/"), LabelBinarizer(), SelectorProxy(SelectFromModel(RandomForestRegressor(random_state = 13, n_estimators = 3), threshold = "1.25 * mean"))]),
(["displacement", "horsepower", "weight", "acceleration"], [ContinuousDomain(), StandardScaler()]),
(["weight", "displacement"], ExpressionTransformer("(X[0] / X[1]) + 0.5"), {"alias" : "weight / displacement + 0.5"})
])
pipeline = PMMLPipeline([
("mapper", mapper),
("selector", SelectUnique()),
("regressor", regressor)
])
pipeline.fit(auto_X, auto_y)
pipeline.configure(**pmml_options)
if isinstance(regressor, XGBRegressor):
pipeline.verify(auto_X.sample(frac = 0.05, random_state = 13), precision = 1e-5, zeroThreshold = 1e-5)
else:
pipeline.verify(auto_X.sample(frac = 0.05, random_state = 13))
store_pkl(pipeline, name)
mpg = DataFrame(pipeline.predict(auto_X), columns = ["mpg"])
store_csv(mpg, name)
def build_auto(regressor, name, fit_params = {}, predict_params = {}, **pmml_options):
cylinders_origin_mapping = {
(8, 1) : "8/1",
(6, 1) : "6/1",
(4, 1) : "4/1",
(6, 2) : "6/2",
(4, 2) : "4/2",
(4, 3) : "4/3"
}
mapper = DataFrameMapper([
(["cylinders"], [CategoricalDomain(), Alias(ExpressionTransformer("X[0] % 2.0 > 0.0", dtype = numpy.int8), name = "odd(cylinders)", prefit = True)]),
(["cylinders", "origin"], [MultiDomain([None, CategoricalDomain()]), MultiLookupTransformer(cylinders_origin_mapping, default_value = "other"), OneHotEncoder()]),
(["model_year"], [CategoricalDomain(), CastTransformer(str), ExpressionTransformer("'19' + X[0] + '-01-01'"), CastTransformer("datetime64[D]"), DaysSinceYearTransformer(1977), Binarizer(threshold = 0)], {"alias" : "bin(model_year, 1977)"}),
(["model_year", "origin"], [ConcatTransformer("/"), OneHotEncoder(sparse = False), SelectorProxy(SelectFromModel(RandomForestRegressor(n_estimators = 3, random_state = 13), threshold = "1.25 * mean"))]),
(["weight", "displacement"], [ContinuousDomain(), ExpressionTransformer("(X[0] / X[1]) + 0.5", dtype = numpy.float64)], {"alias" : "weight / displacement + 0.5"}),
(["displacement", "horsepower", "weight", "acceleration"], [MultiDomain([None, ContinuousDomain(), None, ContinuousDomain()]), StandardScaler()])
])
pipeline = PMMLPipeline([
("mapper", mapper),
("selector", SelectUnique()),
("regressor", regressor)
])
pipeline.fit(auto_X, auto_y, **fit_params)
pipeline.configure(**pmml_options)
if isinstance(regressor, XGBRegressor):
pipeline.verify(auto_X.sample(frac = 0.05, random_state = 13), predict_params = predict_params, precision = 1e-5, zeroThreshold = 1e-5)
else:
pipeline.verify(auto_X.sample(frac = 0.05, random_state = 13), predict_params = predict_params)
store_pkl(pipeline, name)
mpg = DataFrame(pipeline.predict(auto_X, **predict_params), columns = ["mpg"])
store_csv(mpg, name)
def test_sequence_transform(self):
mapper = DataFrameMapper([
(["a"], [ExpressionTransformer("0 if pandas.isnull(X[0]) else X[0]"), Imputer(missing_values = 0)])
])
X = DataFrame([[None], [1], [None]], columns = ["a"])
Xt = mapper.fit_transform(X)
self.assertEqual([[1], [1], [1]], Xt.tolist())
def test_timedelta_seconds(self):
X = DataFrame([["2018-12-31T23:59:59", "2019-01-01T00:00:00"], ["2019-01-01T03:30:03", "2019-01-01T00:00:00"]], columns = ["left", "right"])
mapper = DataFrameMapper([
(["left", "right"], [DateTimeDomain(), SecondsSinceYearTransformer(year = 2010), ExpressionTransformer("X[0] - X[1]")])
])
Xt = mapper.fit_transform(X)
self.assertEqual([[-1], [12603]], Xt.tolist())
def build_auto(regressor, name, **kwargs):
mapper = DataFrameMapper([
(["cylinders"], CategoricalDomain()),
(["displacement", "horsepower", "weight",
"acceleration"], [ContinuousDomain(),
StandardScaler()]),
(["model_year"], [CategoricalDomain(),
Binarizer(threshold=77)], {
"alias": "bin(model_year, 77)"
}), # Pre/post 1973 oil crisis effects
(["origin"], OneHotEncoder()),
(["weight", "displacement"],
ExpressionTransformer("(X[:, 0] / X[:, 1]) + 0.5"), {
"alias": "weight / displacement + 0.5"
})
])
pipeline = Pipeline([("mapper", mapper), ("regressor", regressor)])
pipeline.fit(auto_X, auto_y)
pipeline = make_pmml_pipeline(pipeline, auto_X.columns.values, auto_y.name)
if isinstance(regressor, XGBRegressor):
pipeline.verify(auto_X.sample(frac=0.05, random_state=13),
precision=1e-5,
zeroThreshold=1e-5)
else:
pipeline.verify(auto_X.sample(frac=0.05, random_state=13))
customize(regressor, **kwargs)
store_pkl(pipeline, name + ".pkl")
mpg = DataFrame(pipeline.predict(auto_X), columns=["mpg"])
store_csv(mpg, name + ".csv")
def test_timedelta_days(self):
X = DataFrame(
[["2018-12-31", "2019-01-01"], ["2019-01-31", "2019-01-01"]],
columns=["left", "right"])
pipeline = Pipeline([
("union",
FeatureUnion([
("left_mapper",
DataFrameMapper([
("left",
[DateDomain(),
DaysSinceYearTransformer(year=2010)])
])),
("right_mapper",
DataFrameMapper([
("right",
[DateDomain(),
DaysSinceYearTransformer(year=2010)])
]))
])),
("expression",
Alias(ExpressionTransformer("X[0] - X[1]"),
"delta(left, right)",
prefit=True))
])
Xt = pipeline.fit_transform(X)
self.assertEqual([[-1], [30]], Xt.tolist())
def build_auto(regressor, name, **pmml_options):
cylinders_origin_mapping = {
(8, 1) : "8/1",
(6, 1) : "6/1",
(4, 1) : "4/1",
(6, 2) : "6/2",
(4, 2) : "4/2",
(6, 3) : "6/3",
(4, 3) : "4/3"
}
mapper = DataFrameMapper([
(["cylinders", "origin"], [MultiDomain([CategoricalDomain(), CategoricalDomain()]), MultiLookupTransformer(cylinders_origin_mapping, default_value = "other"), LabelBinarizer()]),
(["model_year"], [CategoricalDomain(), Binarizer(threshold = 77)], {"alias" : "bin(model_year, 77)"}), # Pre/post 1973 oil crisis effects
(["displacement", "horsepower", "weight", "acceleration"], [ContinuousDomain(), StandardScaler()]),
(["weight", "displacement"], ExpressionTransformer("(X[0] / X[1]) + 0.5"), {"alias" : "weight / displacement + 0.5"})
])
pipeline = Pipeline([
("mapper", mapper),
("regressor", regressor)
])
pipeline.fit(auto_X, auto_y)
pipeline = make_pmml_pipeline(pipeline, auto_X.columns.values, auto_y.name)
pipeline.configure(**pmml_options)
if isinstance(regressor, XGBRegressor):
pipeline.verify(auto_X.sample(frac = 0.05, random_state = 13), precision = 1e-5, zeroThreshold = 1e-5)
else:
pipeline.verify(auto_X.sample(frac = 0.05, random_state = 13))
store_pkl(pipeline, name + ".pkl")
mpg = DataFrame(pipeline.predict(auto_X), columns = ["mpg"])
store_csv(mpg, name + ".csv")
def build_audit_na(classifier, name, with_proba = True, fit_params = {}, predict_params = {}, predict_proba_params = {}, predict_transformer = None, predict_proba_transformer = None, apply_transformer = None, **pmml_options):
employment_mapping = {
"CONSULTANT" : "PRIVATE",
"PSFEDERAL" : "PUBLIC",
"PSLOCAL" : "PUBLIC",
"PSSTATE" : "PUBLIC",
"SELFEMP" : "PRIVATE",
"PRIVATE" : "PRIVATE"
}
gender_mapping = {
"FEMALE" : 0.0,
"MALE" : 1.0,
"MISSING_VALUE" : 0.5
}
mapper = DataFrameMapper(
[(["Age"], [ContinuousDomain(missing_values = None, with_data = False), Alias(ExpressionTransformer("X[0] if pandas.notnull(X[0]) else -999", dtype = int), name = "flag_missing(Age, -999)"), SimpleImputer(missing_values = -999, strategy = "constant", fill_value = 38)])] +
[(["Age"], MissingIndicator())] +
[(["Hours"], [ContinuousDomain(missing_values = None, with_data = False), Alias(ExpressionTransformer("-999 if pandas.isnull(X[0]) else X[0]"), name = "flag_missing(Hours, -999)"), SimpleImputer(missing_values = -999, add_indicator = True)])] +
[(["Income"], [ContinuousDomain(missing_values = None, outlier_treatment = "as_missing_values", low_value = 5000, high_value = 200000, with_data = False), SimpleImputer(strategy = "median", add_indicator = True)])] +
[(["Employment"], [CategoricalDomain(missing_values = None, with_data = False), CategoricalImputer(missing_values = None), StringNormalizer(function = "uppercase"), LookupTransformer(employment_mapping, "OTHER"), StringNormalizer(function = "lowercase"), PMMLLabelBinarizer()])] +
[([column], [CategoricalDomain(missing_values = None, missing_value_replacement = "N/A", with_data = False), SimpleImputer(missing_values = "N/A", strategy = "most_frequent"), StringNormalizer(function = "lowercase"), PMMLLabelBinarizer()]) for column in ["Education", "Marital", "Occupation"]] +
[(["Gender"], [CategoricalDomain(missing_values = None, with_data = False), SimpleImputer(strategy = "constant"), StringNormalizer(function = "uppercase"), LookupTransformer(gender_mapping, None)])]
)
pipeline = PMMLPipeline([
("mapper", mapper),
("classifier", classifier)
], predict_transformer = predict_transformer, predict_proba_transformer = predict_proba_transformer, apply_transformer = apply_transformer)
pipeline.fit(audit_na_X, audit_na_y, **fit_params)
pipeline.configure(**pmml_options)
if isinstance(classifier, XGBClassifier):
pipeline.verify(audit_na_X.sample(frac = 0.05, random_state = 13), predict_params = predict_params, predict_proba_params = predict_proba_params, precision = 1e-5, zeroThreshold = 1e-5)
else:
pipeline.verify(audit_na_X.sample(frac = 0.05, random_state = 13), predict_params = predict_params, predict_proba_params = predict_proba_params)
store_pkl(pipeline, name)
adjusted = DataFrame(pipeline.predict(audit_na_X, **predict_params), columns = ["Adjusted"])
if with_proba == True:
adjusted_proba = DataFrame(pipeline.predict_proba(audit_na_X, **predict_proba_params), columns = ["probability(0)", "probability(1)"])
adjusted = pandas.concat((adjusted, adjusted_proba), axis = 1)
if isinstance(classifier, DecisionTreeClassifier):
Xt = pipeline_transform(pipeline, audit_na_X)
adjusted_apply = DataFrame(classifier.apply(Xt), columns = ["nodeId"])
adjusted = pandas.concat((adjusted, adjusted_apply), axis = 1)
store_csv(adjusted, name)
def build_audit_na(classifier, name, with_proba = True, predict_proba_transformer = None, apply_transformer = None, **pmml_options):
employment_mapping = {
"CONSULTANT" : "PRIVATE",
"PSFEDERAL" : "PUBLIC",
"PSLOCAL" : "PUBLIC",
"PSSTATE" : "PUBLIC",
"SELFEMP" : "PRIVATE",
"PRIVATE" : "PRIVATE"
}
gender_mapping = {
"FEMALE" : 0,
"MALE" : 1
}
mapper = DataFrameMapper(
[(["Age"], [ContinuousDomain(missing_values = None, with_data = False), Alias(ExpressionTransformer("X[0] if pandas.notnull(X[0]) else -999"), name = "flag_missing(Age, -999)"), Imputer(missing_values = -999)])] +
[(["Hours"], [ContinuousDomain(missing_values = None, with_data = False), Alias(ExpressionTransformer("-999 if pandas.isnull(X[0]) else X[0]"), name = "flag_missing(Hours, -999)"), Imputer(missing_values = -999)])] +
[(["Income"], [ContinuousDomain(missing_values = None, outlier_treatment = "as_missing_values", low_value = 5000, high_value = 200000, with_data = False), Imputer()])] +
[(["Employment"], [CategoricalDomain(missing_values = None, with_data = False), CategoricalImputer(), StringNormalizer(function = "uppercase"), LookupTransformer(employment_mapping, "OTHER"), StringNormalizer(function = "lowercase"), PMMLLabelBinarizer()])] +
[([column], [CategoricalDomain(missing_values = None, with_data = False), CategoricalImputer(missing_values = None), StringNormalizer(function = "lowercase"), PMMLLabelBinarizer()]) for column in ["Education", "Marital", "Occupation"]] +
[(["Gender"], [CategoricalDomain(missing_values = None, with_data = False), CategoricalImputer(), StringNormalizer(function = "uppercase"), LookupTransformer(gender_mapping, None)])]
)
pipeline = PMMLPipeline([
("mapper", mapper),
("classifier", classifier)
], predict_proba_transformer = predict_proba_transformer, apply_transformer = apply_transformer)
pipeline.fit(audit_na_X, audit_na_y)
pipeline.configure(**pmml_options)
store_pkl(pipeline, name + ".pkl")
adjusted = DataFrame(pipeline.predict(audit_na_X), columns = ["Adjusted"])
if with_proba == True:
adjusted_proba = DataFrame(pipeline.predict_proba(audit_na_X), columns = ["probability(0)", "probability(1)"])
adjusted = pandas.concat((adjusted, adjusted_proba), axis = 1)
if isinstance(classifier, DecisionTreeClassifier):
Xt = pipeline_transform(pipeline, audit_na_X)
adjusted_apply = DataFrame(classifier.apply(Xt), columns = ["nodeId"])
adjusted = pandas.concat((adjusted, adjusted_apply), axis = 1)
store_csv(adjusted, name + ".csv")
def build_auto(regressor, name, **kwargs):
mapper = DataFrameMapper([
(["cylinders"], CategoricalDomain()),
(["displacement", "horsepower", "weight", "acceleration"], [ContinuousDomain(missing_values = None), Imputer(missing_values = "NaN"), StandardScaler()]),
(["model_year"], [CategoricalDomain(), Binarizer(threshold = 77)], {"alias" : "bin(model_year, 77)"}), # Pre/post 1973 oil crisis effects
(["origin"], OneHotEncoder()),
(["weight", "displacement"], ExpressionTransformer("(X[:, 0] / X[:, 1]) + 0.5"), {"alias" : "weight / displacement + 0.5"})
])
pipeline = PMMLPipeline([
("mapper", mapper),
("regressor", regressor)
])
pipeline.fit(auto_X, auto_y)
customize(regressor, **kwargs)
store_pkl(pipeline, name + ".pkl")
mpg = DataFrame(pipeline.predict(auto_X), columns = ["mpg"])
store_csv(mpg, name + ".csv")
adjusted_proba = DataFrame(
pipeline.predict_proba(audit_na_X),
columns=["probability(0)", "probability(1)"])
adjusted = pandas.concat((adjusted, adjusted_proba), axis=1)
if isinstance(classifier, DecisionTreeClassifier):
Xt = pipeline_transform(pipeline, audit_na_X)
adjusted_apply = DataFrame(classifier.apply(Xt), columns=["nodeId"])
adjusted = pandas.concat((adjusted, adjusted_apply), axis=1)
store_csv(adjusted, name + ".csv")
if "Audit" in datasets:
build_audit_na(DecisionTreeClassifier(random_state=13, min_samples_leaf=5),
"DecisionTreeAuditNA",
apply_transformer=Alias(
ExpressionTransformer("X[:, 0] - 1"),
"eval(nodeId)",
prefit=True),
winner_id=True,
class_extensions={"event": {
"0": False,
"1": True
}})
build_audit_na(
LogisticRegression(solver="newton-cg", max_iter=500),
"LogisticRegressionAuditNA",
predict_proba_transformer=Alias(
ExpressionTransformer("numpy.where(X[:, 1] > 0.75, 1, 0)"),
name="eval(probability(1))",
prefit=True))
pipeline.verify(audit_na_X.sample(frac = 0.05, random_state = 13), predict_params = predict_params, predict_proba_params = predict_proba_params, precision = 1e-5, zeroThreshold = 1e-5)
else:
pipeline.verify(audit_na_X.sample(frac = 0.05, random_state = 13), predict_params = predict_params, predict_proba_params = predict_proba_params)
store_pkl(pipeline, name)
adjusted = DataFrame(pipeline.predict(audit_na_X, **predict_params), columns = ["Adjusted"])
if with_proba == True:
adjusted_proba = DataFrame(pipeline.predict_proba(audit_na_X, **predict_proba_params), columns = ["probability(0)", "probability(1)"])
adjusted = pandas.concat((adjusted, adjusted_proba), axis = 1)
if isinstance(classifier, DecisionTreeClassifier):
Xt = pipeline_transform(pipeline, audit_na_X)
adjusted_apply = DataFrame(classifier.apply(Xt), columns = ["nodeId"])
adjusted = pandas.concat((adjusted, adjusted_apply), axis = 1)
store_csv(adjusted, name)
if "Audit" in datasets:
build_audit_na(DecisionTreeClassifier(min_samples_leaf = 5, random_state = 13), "DecisionTreeAuditNA", apply_transformer = Alias(ExpressionTransformer("X[0] - 1"), "eval(nodeId)", prefit = True), winner_id = True, class_extensions = {"event" : {"0" : False, "1" : True}})
build_audit_na(LogisticRegression(multi_class = "ovr", solver = "newton-cg", max_iter = 500), "LogisticRegressionAuditNA", predict_proba_transformer = Alias(ExpressionTransformer("1 if X[1] > 0.75 else 0"), name = "eval(probability(1))", prefit = True))
build_audit_na(XGBClassifier(objective = "binary:logistic", random_state = 13), "XGBAuditNA", predict_params = {"ntree_limit" : 71}, predict_proba_params = {"ntree_limit" : 71}, predict_transformer = Alias(ExpressionTransformer("X[0]"), name = "eval(Adjusted)", prefit = True), ntree_limit = 71)
def build_audit_na_hist(classifier, name):
mapper = DataFrameMapper(
[([column], ContinuousDomain()) for column in ["Age", "Hours", "Income"]] +
[([column], [CategoricalDomain(), PMMLLabelBinarizer()]) for column in ["Employment", "Education", "Marital", "Occupation", "Gender"]]
)
pipeline = PMMLPipeline([
("mapper", mapper),
("classifier", classifier)
])
pipeline.fit(audit_na_X, audit_na_y)
pipeline.verify(audit_na_X.sample(frac = 0.05, random_state = 13))
store_pkl(pipeline, name)
h2o.init()
mapper = DataFrameMapper([("Education", CategoricalDomain()),
("Employment", CategoricalDomain()),
("Gender", CategoricalDomain()),
("Marital", CategoricalDomain()),
("Occupation", CategoricalDomain()),
("Age", [
ContinuousDomain(),
CutTransformer(bins=[17, 28, 37, 47, 83],
labels=["q1", "q2", "q3", "q4"])
]), ("Hours", ContinuousDomain()),
("Income", ContinuousDomain()),
(["Hours", "Income"],
Alias(ExpressionTransformer("X[1] / (X[0] * 52)"),
"Hourly_Income"))])
classifier = H2ORandomForestEstimator(ntrees=17)
predict_proba_transformer = Pipeline([
("expression", ExpressionTransformer("X[1]")),
("cut",
Alias(CutTransformer(bins=[0.0, 0.75, 0.90, 1.0],
labels=["no", "maybe", "yes"]),
"Decision",
prefit=True))
])
pipeline = PMMLPipeline([("local_mapper", mapper),
("uploader", H2OFrameCreator()),
("remote_classifier", classifier)],
def test_transform(self):
transformer = ExpressionTransformer("X['a'] + X['b']", dtype=int)
self.assertTrue(hasattr(transformer, "expr"))
self.assertTrue(hasattr(transformer, "dtype"))
X = DataFrame([[0.5, 0.5], [1.0, 2.0]], columns=["a", "b"])
Xt = transformer.fit_transform(X)
self.assertIsInstance(Xt, numpy.ndarray)
self.assertEqual(int, Xt.dtype)
self.assertEqual([[1], [3]], Xt.tolist())
transformer = ExpressionTransformer("X['a'] + X['b']", dtype=float)
Xt = transformer.fit_transform(X)
self.assertIsInstance(Xt, numpy.ndarray)
self.assertEqual(float, Xt.dtype)
self.assertEqual([[1.0], [3.0]], Xt.tolist())
transformer = ExpressionTransformer("X[0] + X[1]")
self.assertTrue(hasattr(transformer, "expr"))
self.assertTrue(hasattr(transformer, "dtype"))
self.assertIsNone(transformer.dtype)
X = numpy.array([[0.5, 0.5], [1.0, 2.0]])
Xt = transformer.fit_transform(X)
self.assertIsInstance(Xt, numpy.ndarray)
self.assertEqual([[1], [3]], Xt.tolist())
transformer = ExpressionTransformer("X[0] - X[1]")
self.assertEqual([[0.0], [-1.0]],
transformer.fit_transform(X).tolist())
transformer = ExpressionTransformer("X[0] * X[1]")
self.assertEqual([[0.25], [2.0]],
transformer.fit_transform(X).tolist())
transformer = ExpressionTransformer("X[0] / X[1]")
self.assertEqual([[1.0], [0.5]], transformer.fit_transform(X).tolist())
def test_transform(self):
transformer = ExpressionTransformer("X[:, 0] + X[:, 1]")
self.assertTrue(hasattr(transformer, "expr_"))
X = numpy.array([[0.5, 0.5], [1.0, 2.0]])
Xt = transformer.fit_transform(X)
self.assertEqual([1.0, 3.0], Xt.tolist())
transformer = ExpressionTransformer("X[:, 0] - X[:, 1]")
Xt = transformer.fit_transform(X)
self.assertEqual([0.0, -1.0], Xt.tolist())
transformer = ExpressionTransformer("X[:, 0] * X[:, 1]")
Xt = transformer.fit_transform(X)
self.assertEqual([0.25, 2.0], Xt.tolist())
transformer = ExpressionTransformer("X[:, 0] / X[:, 1]")
Xt = transformer.fit_transform(X)
self.assertEqual([1.0, 0.5], Xt.tolist())
("Employment",
[CategoricalDomain(),
LabelBinarizer(),
SelectKBest(chi2, k=3)]),
("Occupation",
[CategoricalDomain(),
LabelBinarizer(),
SelectKBest(chi2, k=3)]),
("Age", [
ContinuousDomain(),
CutTransformer(bins=[17, 28, 37, 47, 83],
labels=["q1", "q2", "q3", "q4"]),
LabelBinarizer()
]), ("Hours", ContinuousDomain()), ("Income", ContinuousDomain()),
(["Hours", "Income"],
Alias(ExpressionTransformer("X[1] / (X[0] * 52)"), "Hourly_Income"))
])
interaction_mapper = DataFrameMapper([
("Gender", [CategoricalDomain(), LabelBinarizer()]),
("Marital", [CategoricalDomain(), LabelBinarizer()])
])
classifier = XGBClassifier()
pipeline = PMMLPipeline([
("mapper",
FeatureUnion([("scalar_mapper", scalar_mapper),
("interaction",
Pipeline([("interaction_mapper", interaction_mapper),
("polynomial", PolynomialFeatures())]))])),
("classifier", classifier)
])
if with_proba == True:
adjusted_proba = DataFrame(
pipeline.predict_proba(audit_na_X),
columns=["probability(0)", "probability(1)"])
adjusted = pandas.concat((adjusted, adjusted_proba), axis=1)
if isinstance(classifier, DecisionTreeClassifier):
Xt = pipeline_transform(pipeline, audit_na_X)
adjusted_apply = DataFrame(classifier.apply(Xt), columns=["nodeId"])
adjusted = pandas.concat((adjusted, adjusted_apply), axis=1)
store_csv(adjusted, name + ".csv")
if "Audit" in datasets:
build_audit_na(DecisionTreeClassifier(random_state=13, min_samples_leaf=5),
"DecisionTreeAuditNA",
apply_transformer=Alias(ExpressionTransformer("X[0] - 1"),
"eval(nodeId)",
prefit=True),
winner_id=True,
class_extensions={"event": {
"0": False,
"1": True
}})
build_audit_na(LogisticRegression(solver="newton-cg", max_iter=500),
"LogisticRegressionAuditNA",
predict_proba_transformer=Alias(
ExpressionTransformer("1 if X[1] > 0.75 else 0"),
name="eval(probability(1))",
prefit=True))
build_audit_na(OptimalXGBClassifier(objective="binary:logistic",
ntree_limit=11,
pipeline.fit(audit_na_X, audit_na_y, **fit_params)
pipeline.configure(**pmml_options)
pipeline.verify(audit_na_X.sample(frac = 0.05, random_state = 13), predict_params = predict_params, predict_proba_params = predict_proba_params)
store_pkl(pipeline, name)
adjusted = DataFrame(pipeline.predict(audit_na_X, **predict_params), columns = ["Adjusted"])
if with_proba == True:
adjusted_proba = DataFrame(pipeline.predict_proba(audit_na_X, **predict_proba_params), columns = ["probability(0)", "probability(1)"])
adjusted = pandas.concat((adjusted, adjusted_proba), axis = 1)
if isinstance(classifier, DecisionTreeClassifier):
Xt = pipeline_transform(pipeline, audit_na_X)
adjusted_apply = DataFrame(classifier.apply(Xt), columns = ["nodeId"])
adjusted = pandas.concat((adjusted, adjusted_apply), axis = 1)
store_csv(adjusted, name)
if "Audit" in datasets:
build_audit_na(DecisionTreeClassifier(min_samples_leaf = 5, random_state = 13), "DecisionTreeAuditNA", apply_transformer = Alias(ExpressionTransformer("X[0] - 1"), "eval(nodeId)", prefit = True), winner_id = True, class_extensions = {"event" : {"0" : False, "1" : True}})
build_audit_na(LogisticRegression(solver = "newton-cg", max_iter = 500), "LogisticRegressionAuditNA", predict_proba_transformer = Alias(ExpressionTransformer("1 if X[1] > 0.75 else 0"), name = "eval(probability(1))", prefit = True))
def build_audit_na_direct(classifier, name):
mapper = DataFrameMapper([
(["Age", "Hours", "Income"], None),
(["Employment", "Education", "Marital", "Occupation", "Gender"], OneHotEncoder())
])
pipeline = PMMLPipeline([
("mapper", mapper),
("classifier", classifier)
])
pipeline.fit(audit_na_X, audit_na_y)
pipeline.verify(audit_na_X.sample(frac = 0.05, random_state = 13), precision = 1e-5, zeroThreshold = 1e-5)
store_pkl(pipeline, name)
adjusted = DataFrame(pipeline.predict(audit_na_X), columns = ["Adjusted"])
store_csv(df, "Apollo")
def build_apollo(mapper, name):
pipeline = PMMLPipeline([("mapper", mapper),
("classifier", DecisionTreeClassifier())])
pipeline.fit(df, df["success"])
store_pkl(pipeline, name)
success = DataFrame(pipeline.predict(df), columns=["success"])
success_proba = DataFrame(
pipeline.predict_proba(df),
columns=["probability(false)", "probability(true)"])
success = pandas.concat((success, success_proba), axis=1)
store_csv(success, name)
mapper = DataFrameMapper([(["launch", "return"], [
DateTimeDomain(),
DaysSinceYearTransformer(year=1968),
ExpressionTransformer("X[1] - X[0]")
])])
build_apollo(mapper, "DurationInDaysApollo")
mapper = DataFrameMapper([(["launch", "return"], [
DateTimeDomain(),
SecondsSinceYearTransformer(year=1968),
ExpressionTransformer("X[1] - X[0]")
])])
build_apollo(mapper, "DurationInSecondsApollo")
result = pandas.concat([result, probabilities], axis=1)
store_csv(result, name)
audit_data = load_audit("Audit")
audit_feature_pipeline = Pipeline([
("mapper",
DataFrameMapper(
[(cat_column, [CategoricalDomain(),
LabelBinarizer()]) for cat_column in
["Employment", "Education", "Marital", "Occupation", "Gender"]] +
[(cont_column, ContinuousDomain())
for cont_column in ["Age", "Income", "Hours"]] +
[(["Income", "Hours"],
Alias(ExpressionTransformer("X[0] / (X[1] * 52.0)"),
"Hourly_Income",
prefit=True))],
df_out=True))
])
build_classifier(audit_data, audit_feature_pipeline, 3, 7, "TPOTAudit")
iris_data = load_iris("Iris")
iris_feature_pipeline = Pipeline([
("mapper",
DataFrameMapper([(iris_data[0].columns.values, ContinuousDomain())]))
])
build_classifier(iris_data, iris_feature_pipeline, 7, 17, "TPOTIris")
