In [ ]:

    

import logging
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
import tensorframes as tfs
import tensorflow as tf
from pyspark.sql import Row
from pyspark.sql.functions import *
from pyspark.sql.types import DoubleType, IntegerType, LongType, FloatType

from tensorframes.core import _java_api
japi = _java_api()
_java_api().initialize_logging()


# The input data
data = [Row(x=[float(x), float(2 * x)], key=str(x % 2)) for x in range(1, 6)]
df = sqlContext.createDataFrame(data)
df = tfs.analyze(sqlContext.createDataFrame(data))

# The geometric mean:
# TODO(tjh) make a test out of this, it found some bugs
# - non numeric columns (string)
# - unused columns
# - output that has a child
col_name = "x"
col_key = "key"
with tf.Graph().as_default() as g:
    x = tfs.block(df, col_name)
    invs = tf.inv(tf.to_double(x), name="invs")
    df2 = tfs.map_blocks([invs, tf.ones_like(invs, name="count")], df)


# The geometric mean
gb = df2.select(col_key, "invs", "count").groupBy("key")
with tf.Graph().as_default() as g:
    x_input = tfs.block(df2, "invs", tf_name="invs_input")
    count_input = tfs.block(df2, "invs", tf_name="count_input")
    x = tf.reduce_sum(x_input, [0], name='invs')
    count = tf.reduce_sum(count_input, [0], name='count')
    df3 = tfs.aggregate([x, count], gb)

with tf.Graph().as_default() as g:
    invs = tfs.block(df2, "invs")
    count = tfs.block(df2, "count")
    geom_mean = tf.div(tf.to_double(count), invs,  name = "harmonic_mean")
    df4 = tfs.map_blocks(geom_mean, df3).select("key", "harmonic_mean")

df4.collect()


with tf.Graph().as_default() as g:
    x = tf.zeros([], tf.int32)
    y = tf.zeros(x)
    print y.graph.as_graph_def()

with tf.Graph().as_default() as g:
    x = tf.zeros([1, 3], tf.double)
    y = tf.nn.l2_normalize(x, [0])
    print y.graph.as_graph_def()

with tf.Graph().as_default() as g:
    x = tf.zeros([1, 3], tf.double)
    y = tf.transpose(x)
    print y.graph.as_graph_def()

with tf.Graph().as_default() as g:
    tf.zeros([], tf.int32)
    tf.zeros([], tf.int32)
    with tf.variable_scope("scope"):
        tf.zeros([], tf.int32)
        tf.ones([], tf.int32)
        tf.ones([], tf.int32)
        x = tf.zeros([], tf.int32)
    print x.graph.as_graph_def()

x = tf.ones([3, 2, 1])
y = tf.reduce_sum(x, [1])
z = tf.transpose(tf.transpose(x) / y)

x = tf.placeholder(tf.float32, [None, 3])
y = tf.reduce_sum(x, [0])

x / y

for n in g.node:
    print ">>>>>", str(n.name), "<<<<<<"
    print n