mainAutoEncoderAnalysis.py

# Copyright 2017 The TensorFlow 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.
# ==============================================================================
"""Example code for TensorFlow Wide & Deep Tutorial using tf.estimator API."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import os
import shutil
import string

from absl import app as absl_app
from absl import flags
import tensorflow as tf  # pylint: disable=g-bad-import-order

tf_base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + '/models';

print(tf_base_dir)
if not tf_base_dir in sys.path:
    sys.path.append(tf_base_dir);

DIRPROJECT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + '/';

from official.utils.flags import core as flags_core
from official.utils.logs import hooks_helper
from official.utils.logs import logger
from official.utils.misc import model_helpers

from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.manifold import TSNE

import matplotlib.pyplot as plt
import numpy as np

from learning.neuralnet.AutoEncoderModel import AutoEncoderModel
from learning.neuralnet.FeatureColumnsAutoEncoderNZ import FeatureColumnsAutoEncoderNZ
from learning.neuralnet.FeatureColumnsPatrecFusion import FeatureColumnsPatrecFusion
from utils.DatasetOptions import DatasetOptions



def define_flags():
    """Add supervised learning flags, as well as wide-deep model type."""
    flags_core.define_base()
    flags_core.define_benchmark()

    flags.adopt_module_key_flags(flags_core)

    flags_core.set_defaults(data_dir=DIRPROJECT + 'data/',
                            model_dir='/tmp/patients_model',
                            export_dir='/tmp/patients_model/export_model',
                            train_epochs=250,
                            epochs_between_evals=1,
                            batch_size=160)


def analyze(flags_obj):
    """Run Wide-Deep training and eval loop.
    Args:
    flags_obj: An object containing parsed flag values.
    """
    dict_data_train = {
        'dir_data':             DIRPROJECT + 'data/',
        'data_prefix':          'nz',
        'dataset':              '20072016',
        'encoding':             'embedding',
        'newfeatures':          None,
        'featurereduction':     {'method': 'FUSION'},
        'grouping':             'verylightgrouping'
    }
    dataset_options_train = DatasetOptions(dict_data_train);
    dataset_options_eval = None;

    if dict_data_train['data_prefix'] == 'nz':
        feature_columns = FeatureColumnsAutoEncoderNZ(dataset_options=dataset_options_train);
    else:
        print('unknown data prefix..exit')
        sys.exit()

    dict_dataset_options = {
        'train':    dataset_options_train,
        'eval':     dataset_options_eval,
        'test':     None
    }

    nn = AutoEncoderModel('analysis', dict_dataset_options, feature_columns, flags_obj);
    basic_encodings = nn.analyze();

    num_colors = 26;
    colors = plt.cm.rainbow(np.linspace(0, 1, num_colors));

    pca = PCA(n_components=2)
    weights_2d_pca = pca.fit_transform(basic_encodings);

    tsne = TSNE(n_components=2);
    weights_2d_tsne = tsne.fit_transform(basic_encodings);


    diag_group_names = dataset_options_train.getDiagGroupNames();
    num_diags = len(diag_group_names);

    if dataset_options_train.getGroupingName() == 'verylightgrouping':
        num_subcategories = 100;
    elif dataset_options_train.getGroupingName() == 'lightgrouping':
        num_subcategories = 10;
    elif dataset_options_train.getGroupingName() == 'grouping':
        num_subcategories = 1;
    else:
        print('grouping scheme is unknown...exit')
        sys.exit()


    plt.figure();
    for k in range(0, num_colors):
        c = colors[k]
        plt.scatter(weights_2d_pca[k*num_subcategories:(k*num_subcategories+num_subcategories), 0],
                    weights_2d_pca[k*num_subcategories:(k*num_subcategories+num_subcategories), 1],
                    label=string.ascii_uppercase[k], alpha=0.5, s=100, c=c);
    plt.legend()
    plt.title('pca')
    plt.draw()


    plt.figure();
    for k in range(0, num_colors):
        c = colors[k]
        plt.scatter(weights_2d_tsne[k*num_subcategories:(k*num_subcategories+num_subcategories), 0],
                    weights_2d_tsne[k*num_subcategories:(k*num_subcategories+num_subcategories), 1],
                    label=string.ascii_uppercase[k],alpha=0.5, s=100, c=c);
    plt.legend()
    plt.title('t-sne')
    plt.draw()

    plt.show()



def main(_):
    analyze(flags.FLAGS)


if __name__ == '__main__':

    tf.logging.set_verbosity(tf.logging.INFO)
    define_flags()
    absl_app.run(main)