#

# Copyright 2019 GridGain Systems, Inc. and Contributors.

#

# Licensed under the GridGain Community Edition License (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# https://www.gridgain.com/products/software/community-edition/gridgain-community-edition-license

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

#

# Linear regression with Ignite ML (local).

from sklearn.datasets import make_regression

from sklearn.model_selection import train_test_split

from sklearn.metrics import r2_score

from ggml.regression import LinearRegressionTrainer

x, y = make_regression()

x_train, x_test, y_train, y_test = train_test_split(x, y)

trainer = LinearRegressionTrainer()

model = trainer.fit(x_train, y_train)

r2_score(y_test, model.predict(x_test))

# Linear regression with Ignite ML (cache).

import numpy as np

from sklearn.datasets import make_regression

from ggml.core import Ignite

from ggml.model_selection import train_test_split

from ggml.metrics import rmse_score

from ggml.regression import LinearRegressionTrainer

with Ignite("example-ignite.xml") as ignite:

cache = ignite.create_cache("my-cache")

for i, row in enumerate(np.column_stack(make_regression())):

cache.put(i, row)

train_cache, test_cache = train_test_split(cache)

trainer = LinearRegressionTrainer()

model = trainer.fit(train_cache)

print(rmse_score(test_cache, model))

# Decision Tree regression with Ignite ML (local).

from sklearn.datasets import make_regression

from sklearn.model_selection import train_test_split

from sklearn.metrics import r2_score

from ggml.regression import DecisionTreeRegressionTrainer

x, y = make_regression()

x_train, x_test, y_train, y_test = train_test_split(x, y)

trainer = DecisionTreeRegressionTrainer()

model = trainer.fit(x_train, y_train)

r2_score(y_test, model.predict(x_test))

# Decision Tree regression with Ignite ML (cache).

import numpy as np

from sklearn.datasets import make_regression

from ggml.core import Ignite

from ggml.model_selection import train_test_split

from ggml.metrics import rmse_score

from ggml.regression import DecisionTreeRegressionTrainer

with Ignite("example-ignite.xml") as ignite:

cache = ignite.create_cache("my-cache")

for i, row in enumerate(np.column_stack(make_regression())):

cache.put(i, row)

train_cache, test_cache = train_test_split(cache)

trainer = DecisionTreeRegressionTrainer()

model = trainer.fit(train_cache)

print(rmse_score(test_cache, model))

# KNN regression with Ignite ML (local).

from sklearn.datasets import make_regression

from sklearn.model_selection import train_test_split

from sklearn.metrics import r2_score

from ggml.regression import KNNRegressionTrainer

x, y = make_regression()

x_train, x_test, y_train, y_test = train_test_split(x, y)

trainer = KNNRegressionTrainer()

model = trainer.fit(x_train, y_train)

r2_score(y_test, model.predict(x_test))

# KNN regression with Ignite ML (cache).

import numpy as np

from sklearn.datasets import make_regression

from ggml.core import Ignite

from ggml.model_selection import train_test_split

from ggml.metrics import rmse_score

from ggml.regression import KNNRegressionTrainer

with Ignite("example-ignite.xml") as ignite:

cache = ignite.create_cache("my-cache")

for i, row in enumerate(np.column_stack(make_regression())):

cache.put(i, row)

train_cache, test_cache = train_test_split(cache)

trainer = KNNRegressionTrainer()

model = trainer.fit(train_cache)

print(rmse_score(test_cache, model))

# Random Forest regression with Ignite ML (local).

from sklearn.datasets import make_regression

from sklearn.model_selection import train_test_split

from sklearn.metrics import r2_score

from ggml.regression import RandomForestRegressionTrainer

x, y = make_regression()

x_train, x_test, y_train, y_test = train_test_split(x, y)

trainer = RandomForestRegressionTrainer(features=100)

model = trainer.fit(x_train, y_train)

r2_score(y_test, model.predict(x_test))

# Random Forest regression with Ignite ML (cache).

import numpy as np

from sklearn.datasets import make_regression

from ggml.core import Ignite

from ggml.model_selection import train_test_split

from ggml.metrics import rmse_score

from ggml.regression import RandomForestRegressionTrainer

with Ignite("example-ignite.xml") as ignite:

cache = ignite.create_cache("my-cache")

for i, row in enumerate(np.column_stack(make_regression())):

cache.put(i, row)

train_cache, test_cache = train_test_split(cache)

trainer = RandomForestRegressionTrainer(features=100)

model = trainer.fit(train_cache)

print(rmse_score(test_cache, model))

# MLP regression with Ignite ML (local).

from sklearn.datasets import make_regression

from sklearn.model_selection import train_test_split

from sklearn.metrics import r2_score

from ggml.regression import MLPArchitecture

from ggml.regression import MLPRegressionTrainer

x, y = make_regression()

x_train, x_test, y_train, y_test = train_test_split(x, y)

trainer = MLPRegressionTrainer(MLPArchitecture(input_size=100).with_layer(neurons=1, activator='linear'))

model = trainer.fit(x_train, y_train)

r2_score(y_test, model.predict(x_test))

# MLP regression with Ignite ML (cache).

import numpy as np

from sklearn.datasets import make_regression

from ggml.core import Ignite

from ggml.model_selection import train_test_split

from ggml.metrics import rmse_score

from ggml.regression import MLPArchitecture

from ggml.regression import MLPRegressionTrainer

with Ignite("example-ignite.xml") as ignite:

cache = ignite.create_cache("my-cache")

for i, row in enumerate(np.column_stack(make_regression())):

cache.put(i, row)

train_cache, test_cache = train_test_split(cache)

trainer = MLPRegressionTrainer(MLPArchitecture(input_size=100).with_layer(neurons=1, activator='linear'))

model = trainer.fit(train_cache)

print(rmse_score(test_cache, model))