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# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn import datasets, metrics, cross_validation
import pandas as pd
import dask.dataframe as dd
from tensorflow.contrib import learn
# Sometimes when your dataset is too large to hold in the memory
# you may want to load it into a out-of-core dataframe as provided by dask library
# to firstly draw sample batches and then load into memory for training.
# Load dataset.
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
test_size=0.2, random_state=42)
# Note that we use iris here just for demo purposes
# You can load your own large dataset into a out-of-core dataframe
# using dask's methods, e.g. read_csv() in dask
# details please see: http://dask.pydata.org/en/latest/dataframe.html
# We firstly load them into pandas dataframe and then convert into dask dataframe
X_train, y_train, X_test, y_test = [pd.DataFrame(data) for data in [X_train, y_train, X_test, y_test]]
X_train, y_train, X_test, y_test = [dd.from_pandas(data, npartitions=2) for data in [X_train, y_train, X_test, y_test]]
# Initialize a TensorFlow linear classifier
classifier = learn.TensorFlowLinearClassifier(n_classes=3)
# Fit the model using training set
classifier.fit(X_train, y_train)
# Make predictions on each partitions of testing data
predictions = X_test.map_partitions(classifier.predict).compute()
# Calculate accuracy
score = metrics.accuracy_score(y_test.compute(), predictions)
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