I noticed that the time to compute the precision of a model is almost as long as the the time for creating the model itself and this doesn't seem right. I have a cluster with six virtual machines. The most expensive in time is the first iteration from "for item in range(numClasses)" loop. What rdd operations are supposed to happen behind this ?
Code:
%pyspark
from pyspark.sql.types import DoubleType
from pyspark.sql.functions import UserDefinedFunction
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.tree import DecisionTree
from pyspark.mllib.evaluation import MulticlassMetrics
from timeit import default_timer
def decision_tree(train,test,numClasses,CatFeatInf):
ref = default_timer()
training_data = train.rdd.map(lambda row: LabeledPoint(row[-1], row[:-1])).persist(StorageLevel.MEMORY_ONLY)
testing_data = test.rdd.map(lambda row: LabeledPoint(row[-1], row[:-1])).persist(StorageLevel.MEMORY_ONLY)
print 'transformed in dense data in: %.3f seconds'%(default_timer()-ref)
ref = default_timer()
model = DecisionTree.trainClassifier(training_data,
numClasses=numClasses,
maxDepth=7,
categoricalFeaturesInfo=CatFeatInf,
impurity='entropy', maxBins=max(CatFeatInf.values()))
print 'model created in: %.3f seconds'%(default_timer()-ref)
ref = default_timer()
predictions_and_labels = model.predict(testing_data.map(lambda r: r.features)).zip(testing_data.map(lambda r: r.label))
print 'predictions made in: %.3f seconds'%(default_timer()-ref)
ref = default_timer()
metrics = MulticlassMetrics(predictions_and_labels)
res = {}
for item in range(numClasses):
try:
res[item] = metrics.precision(item)
except:
res[item] = 0.0
print 'accuracy calculated in: %.3f seconds'%(default_timer()-ref)
return res
transformed in dense data in: 0.074 seconds
model created in: 355.276 seconds
predictions made in: 0.095 seconds
accuracy calculated in: 346.497 seconds
It may be possible that are some unfinished rdd operations that are executed when I first call metrics.precision(0)
Related
I am learning neural network modeling and its uses in time series prediction.
First, thank you for reading this post and for your help :)
On this page there are various NN models (LSTM, CNN etc.) for predicting "traffic volume":
https://michael-fuchs-python.netlify.app/2020/11/01/time-series-analysis-neural-networks-for-forecasting-univariate-variables/#train-validation-split
I got inspired and decided to use/shorten/adapt the code in there for a problem of my own: predicting the bitcoin price.
I have the bitcoin daily prices starting 1.1.2017
in total 2024 daily prices
I use the first 85% of the data for the training data, and the rest as the validation (except the last 10 observation, which I would like to use as test data to see how good my model is)
I would like to use a Feedforward model
My goal is merely having a code that runs.
I have managed so far to have most of my code run. However, I get a strange format for my test forecast results: It should be simply an array of 10 numbers (i.e. predicted prices corresponding to the 10 day at the end of my data). To my surprise what is printed out is a long list of numbers. I need help to find out what changes I need to make to make to the code to make it run.
Thank you for helping me :)
The code is pasted down there, followed by the error:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import preprocessing #import MinMaxScaler
from sklearn import metrics #import mean_squared_error
import seaborn as sns
sns.set()
import tensorflow as tf
from tensorflow import keras
from keras.layers import Input, Dense, Flatten
from keras.optimizers import Adam
from keras.models import Sequential
from keras.callbacks import EarlyStopping
tf.__version__
df = pd.read_csv('/content/BTC-USD.csv')
def mean_absolute_percentage_error_func(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
def timeseries_evaluation_metrics_func(y_true, y_pred):
print('Evaluation metric results: ')
print(f'MSE is : {metrics.mean_squared_error(y_true, y_pred)}')
print(f'MAE is : {metrics.mean_absolute_error(y_true, y_pred)}')
print(f'RMSE is : {np.sqrt(metrics.mean_squared_error(y_true, y_pred))}')
print(f'MAPE is : {mean_absolute_percentage_error_func(y_true, y_pred)}')
print(f'R2 is : {metrics.r2_score(y_true, y_pred)}',end='\n\n')
def univariate_data_prep_func(dataset, start, end, window, horizon):
X = []
y = []
start = start + window
if end is None:
end = len(dataset) - horizon
for i in range(start, end):
indicesx = range(i-window, i)
X.append(np.reshape(dataset[indicesx], (window, 1)))
indicesy = range(i,i+horizon)
y.append(dataset[indicesy])
return np.array(X), np.array(y)
# Generating the test set
test_data = df['close'].tail(10)
df = df.drop(df['close'].tail(10).index)
df.shape
# Defining the target variable
uni_data = df['close']
uni_data.index = df['formatted_date']
uni_data.head()
#scaling
from sklearn import preprocessing
uni_data = uni_data.values
scaler_x = preprocessing.MinMaxScaler()
x_scaled = scaler_x.fit_transform(uni_data.reshape(-1, 1))
# Single Step Style (sss) modeling
univar_hist_window_sss = 50
horizon_sss = 1
# 2014 observations in total
# 2014*0.85=1710 should be part of the training (304 validation)
train_split_sss = 1710
x_train_uni_sss, y_train_uni_sss = univariate_data_prep_func(x_scaled, 0, train_split_sss,
univar_hist_window_sss, horizon_sss)
x_val_uni_sss, y_val_uni_sss = univariate_data_prep_func(x_scaled, train_split_sss, None,
univar_hist_window_sss, horizon_sss)
print ('Length of first Single Window:')
print (len(x_train_uni_sss[0]))
print()
print ('Target horizon:')
print (y_train_uni_sss[0])
BATCH_SIZE_sss = 32
BUFFER_SIZE_sss = 150
train_univariate_sss = tf.data.Dataset.from_tensor_slices((x_train_uni_sss, y_train_uni_sss))
train_univariate_sss = train_univariate_sss.cache().shuffle(BUFFER_SIZE_sss).batch(BATCH_SIZE_sss).repeat()
validation_univariate_sss = tf.data.Dataset.from_tensor_slices((x_val_uni_sss, y_val_uni_sss))
validation_univariate_sss = validation_univariate_sss.batch(BATCH_SIZE_sss).repeat()
n_steps_per_epoch = 55
n_validation_steps = 10
n_epochs = 100
#FFNN architecture
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(8, input_shape=x_train_uni_sss.shape[-2:]),
tf.keras.layers.Dense(units=horizon_sss)])
model.compile(loss='mse',
optimizer='adam')
#fit the model
model_path = '/content/FFNN_model_sss.h5'
keras_callbacks = [tf.keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=0, patience=10,
verbose=1, mode='min'),
tf.keras.callbacks.ModelCheckpoint(model_path,monitor='val_loss',
save_best_only=True,
mode='min', verbose=0)]
history = model.fit(train_univariate_sss, epochs=n_epochs, steps_per_epoch=n_steps_per_epoch,
validation_data=validation_univariate_sss, validation_steps=n_validation_steps, verbose =1,
callbacks = keras_callbacks)
#validation
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(loss) + 1)
plt.plot(epochs, loss, 'r', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
# Testing our model
trained_ffnn_model_sss = tf.keras.models.load_model(model_path)
df_temp = df['close']
test_horizon = df_temp.tail(univar_hist_window_sss)
test_history = test_horizon.values
result = []
# Define Forecast length here
window_len = len(test_data)
test_scaled = scaler_x.fit_transform(test_history.reshape(-1, 1))
for i in range(1, window_len+1):
test_scaled = test_scaled.reshape((1, test_scaled.shape[0], 1))
# Inserting the model
predicted_results = trained_ffnn_model_sss.predict(test_scaled)
print(f'predicted : {predicted_results}')
result.append(predicted_results[0])
test_scaled = np.append(test_scaled[:,1:],[[predicted_results]])
result_inv_trans = scaler_x.inverse_transform(result)
result_inv_trans
I believe the problem might have to do with the shapes of data. How exactly I do not yet know.
Data:
click here
Traceback:
click here
Hello StackOverflowers.
I have a pyspark dataframe that consists of a time_column and a column with values.
E.g.
+----------+--------------------+
| snapshot| values|
+----------+--------------------+
|2005-01-31| 0.19120256617637743|
|2005-01-31| 0.7972692479278891|
|2005-02-28|0.005236883665445502|
|2005-02-28| 0.5474099672222935|
|2005-02-28| 0.13077227571485905|
+----------+--------------------+
I would like to perform a KS test of each snapshot value with the previous one.
I tried to do it with a for loop.
import numpy as np
from scipy.stats import ks_2samp
import pyspark.sql.functions as F
def KS_for_one_snapshot(temp_df, snapshots_list, j, var = "values"):
sample1=temp_df.filter(F.col("snapshot")==snapshots_list[j])
sample2=temp_df.filter(F.col("snapshot")==snapshots_list[j-1]) # pick the last snapshot as the one to compare with
if (sample1.count() == 0 or sample2.count() == 0 ):
ks_value = -1 # previously "0 observations" which gave type error
else:
ks_value, p_value = ks_2samp( np.array(sample1.select(var).collect()).reshape(-1)
, np.array(sample2.select(var).collect()).reshape(-1)
, alternative="two-sided"
, mode="auto")
return ks_value
results = []
snapshots_list = df.select('snapshot').dropDuplicates().sort('snapshot').rdd.flatMap(lambda x: x).collect()
for j in range(len(snapshots_list) - 1 ):
results.append(KS_for_one_snapshot(df, snapshots_list, j+1))
results
But the data in reality is huge so it takes forever. I am using databricks and pyspark, so I wonder what would be a more efficient way to run it by avoiding the for loop and utilizing the available workers.
I tried to do it by using a udf but in vain.
Any ideas?
PS. you can generate the data with the following code.
from random import randint
df = (spark.createDataFrame( range(1,1000), T.IntegerType())
.withColumn('snapshot' ,F.array(F.lit("2005-01-31"), F.lit("2005-02-28"),F.lit("2005-03-30") ).getItem((F.rand()*3).cast("int")))
.withColumn('values', F.rand()).drop('value')
)
Update:
I tried the following by using an UDF.
var_used = 'values'
data_input_1 = df.groupBy('snapshot').agg(F.collect_list(var_used).alias('value_list'))
data_input_2 = df.groupBy('snapshot').agg(F.collect_list(var_used).alias("value_list_2"))
windowSpec = Window.orderBy("snapshot")
data_input_2 = data_input_2.withColumn('snapshot_2', F.lag("snapshot", 1).over(Window.orderBy('snapshot'))).filter('snapshot_2 is not NULL')
data_input_final = data_input_final = data_input_1.join(data_input_2, data_input_1.snapshot == data_input_2.snapshot_2)
def KS_one_snapshot_general(sample_in_list_1, sample_in_list_2):
if (len(sample_in_list_1) == 0 or len(sample_in_list_2) == 0 ):
ks_value = -1 # previously "0 observations" which gave type error
else:
print('something')
ks_value, p_value = ks_2samp( sample_in_list_1
, sample_in_list_2
, alternative="two-sided"
, mode="auto")
return ks_value
import pyspark.sql.types as T
KS_one_snapshot_general_udf = udf(KS_one_snapshot_general, T.FloatType())
data_input_final.select( KS_one_snapshot_general_udf('value_list', 'value_list_2')).display()
Which works fine if the dataset (per snapshot) is small. But If I increase the number of rows then I end up with an error.
PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)
I want to train a model in distributed system. I have found a code in github for distributed training where the worker node send gradient to the parameter server and the parameter server sends the average gradient to the workers. But in client/worker side code, i couldn't understand where the received gradient updates the weights and biases.
Here is client/worker side the code, it receives initial gradients from the parameter server and then calculates loss, gradients and sends the gradient value to the server again.
from __future__ import division
from __future__ import print_function
import numpy as np
import sys
import pickle as pickle
import socket
from datetime import datetime
import time
import tensorflow as tf
import cifar10
TCP_IP = 'some IP'
TCP_PORT = 5014
port = 0
port_main = 0
s = 0
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('train_dir', '/home/ubuntu/cifar10_train',
"""Directory where to write event logs """
"""and checkpoint.""")
tf.app.flags.DEFINE_integer('max_steps', 5000,
"""Number of batches to run.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
tf.app.flags.DEFINE_integer('log_frequency', 10,
"""How often to log results to the console.""")
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.30)
def safe_recv(size, server_socket):
data = ""
temp = ""
data = bytearray()
recv_size = 0
while 1:
try:
temp = server_socket.recv(size-len(data))
data.extend(temp)
recv_size = len(data)
if recv_size >= size:
break
except:
print("Error")
data = bytes(data)
return data
def train():
"""Train CIFAR-10 for a number of steps."""
g1 = tf.Graph()
with g1.as_default():
global_step = tf.Variable(-1, name='global_step',
trainable=False, dtype=tf.int32)
increment_global_step_op = tf.assign(global_step, global_step+1)
# Get images and labels for CIFAR-10.
images, labels = cifar10.distorted_inputs()
# Build a Graph that computes the logits predictions from the
# inference model.
logits = cifar10.inference(images)
# Calculate loss.
loss = cifar10.loss(logits, labels)
grads = cifar10.train_part1(loss, global_step)
only_gradients = [g for g, _ in grads]
class _LoggerHook(tf.train.SessionRunHook):
"""Logs loss and runtime."""
def begin(self):
self._step = -1
self._start_time = time.time()
def before_run(self, run_context):
self._step += 1
return tf.train.SessionRunArgs(loss) # Asks for loss value.
def after_run(self, run_context, run_values):
if self._step % FLAGS.log_frequency == 0:
current_time = time.time()
duration = current_time - self._start_time
self._start_time = current_time
loss_value = run_values.results
examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
sec_per_batch = float(duration / FLAGS.log_frequency)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
_LoggerHook()],
config=tf.ConfigProto(
# log_device_placement=FLAGS.log_device_placement, gpu_options=gpu_options)) as mon_sess:
log_device_placement=FLAGS.log_device_placement)) as mon_sess:
global port
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((TCP_IP, port_main))
recv_size = safe_recv(17, s)
recv_size = pickle.loads(recv_size)
recv_data = safe_recv(recv_size, s)
var_vals = pickle.loads(recv_data)
s.close()
feed_dict = {}
i = 0
for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
feed_dict[v] = var_vals[i]
i = i+1
print("Received variable values from ps")
# Opening the socket and connecting to server
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((TCP_IP, port))
while not mon_sess.should_stop():
gradients, step_val = mon_sess.run(
[only_gradients, increment_global_step_op], feed_dict=feed_dict)
# sending the gradients
send_data = pickle.dumps(gradients, pickle.HIGHEST_PROTOCOL)
to_send_size = len(send_data)
send_size = pickle.dumps(to_send_size, pickle.HIGHEST_PROTOCOL)
s.sendall(send_size)
s.sendall(send_data)
# receiving the variable values
recv_size = safe_recv(17, s)
recv_size = pickle.loads(recv_size)
recv_data = safe_recv(recv_size, s)
var_vals = pickle.loads(recv_data)
feed_dict = {}
i = 0
for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
feed_dict[v] = var_vals[i]
i = i+1
s.close()
def main(argv=None): # pylint: disable=unused-argument
global port
global port_main
global s
if(len(sys.argv) != 3):
print("<port> <worker-id> required")
sys.exit()
port = int(sys.argv[1]) + int(sys.argv[2])
port_main = int(sys.argv[1])
print("Connecting to port ", port)
cifar10.maybe_download_and_extract()
if tf.gfile.Exists(FLAGS.train_dir):
tf.gfile.DeleteRecursively(FLAGS.train_dir)
tf.gfile.MakeDirs(FLAGS.train_dir)
total_start_time = time.time()
train()
print("--- %s seconds ---" % (time.time() - total_start_time))
if __name__ == '__main__':
tf.app.run()
EDIT:
Here is the train_part1() code:
def train_part1(total_loss, global_step):
"""Train CIFAR-10 model.
Create an optimizer and apply to all trainable variables. Add moving
average for all trainable variables.
Args:
total_loss: Total loss from loss().
global_step: Integer Variable counting the number of training steps
processed.
Returns:
train_op: op for training.
"""
# Variables that affect learning rate.
num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size
decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE,
global_step,
decay_steps,
LEARNING_RATE_DECAY_FACTOR,
staircase=True)
tf.summary.scalar('learning_rate', lr)
# Generate moving averages of all losses and associated summaries.
loss_averages_op = _add_loss_summaries(total_loss)
# Compute gradients.
with tf.control_dependencies([loss_averages_op]):
opt = tf.train.GradientDescentOptimizer(lr)
grads = opt.compute_gradients(total_loss)
return grads
To me it seems that line
gradients, step_val = mon_sess.run(
[only_gradients, increment_global_step_op], feed_dict=feed_dict)
receieves new values for variables in feed_dict, assign these values to variables, and makes a training step, during which it only calculates and returns the gradients, that are later sent to the parameter server. I would expect cifar10.train_part1 (the one that returns only_gradients) to depend on variable values and define the update.
Update: I looked into the code and changed my mind. Had to google and found next answer that shed some light on what is happening.
Gradients are actually not applied in this code anywhere implicitly. Instead, gradients are sent to the parameter server, parameter server averages gradients and applies them to weights, it returns the weights to the local worker, * recieved weights are used instead of local weights during session run through feed_dict* i.e. local weights are never actually updated and do not actually matter at all. The key, is that feed_dict allows to rewrite any tensor output of the session run and this code rewrites variables.
I'm trying to use the chainer package for a large project I'm working on. I have read through the tutorial on their website which gives an example of applying it to the MNIST dataset, but it doesn't seem to scale easily to other examples, and there's simply not enough documentation otherwise.
Their example code is as follows:
class MLP(Chain):
def __init__(self, n_units, n_out):
super(MLP, self).__init__(
# the size of the inputs to each layer will be inferred
l1=L.Linear(None, n_units), # n_in -> n_units
l2=L.Linear(None, n_units), # n_units -> n_units
l3=L.Linear(None, n_out), # n_units -> n_out
)
def __call__(self, x):
h1 = F.relu(self.l1(x))
h2 = F.relu(self.l2(h1))
y = self.l3(h2)
return y
train, test = datasets.get_mnist()
train_iter = iterators.SerialIterator(train, batch_size=5, shuffle=True)
test_iter = iterators.SerialIterator(test, batch_size=2, repeat=False, shuffle=False)
model = L.Classifier(MLP(100, 10)) # the input size, 784, is inferred
optimizer = optimizers.SGD()
optimizer.setup(model)
updater = training.StandardUpdater(train_iter, optimizer)
trainer = training.Trainer(updater, (4, 'epoch'), out='result')
trainer.extend(extensions.Evaluator(test_iter, model))
trainer.extend(extensions.LogReport())
trainer.extend(extensions.PrintReport(['epoch', 'main/accuracy', 'validation/main/accuracy']))
trainer.extend(extensions.ProgressBar())
trainer.run()
Could someone point me in the direction of how to simple fit a straight line to a few data points in 2D? If I can understand a simple fit such as this I should be able to scale appropriately.
Thanks for the help!
I pasted simple regression modeling here.
You can use original train data and test data as tuple.
train = (data, label)
Here, data.shape = (Number of data, Number of data dimesion)
And, label.shape = (Number of data,)
Both of their data type should be numpy.float32.
import chainer
from chainer.functions import *
from chainer.links import *
from chainer.optimizers import *
from chainer import training
from chainer.training import extensions
from chainer import reporter
from chainer import datasets
import numpy
class MyNet(chainer.Chain):
def __init__(self):
super(MyNet, self).__init__(
l0=Linear(None, 30, nobias=True),
l1=Linear(None, 1, nobias=True),
)
def __call__(self, x, t):
l0 = self.l0(x)
f0 = relu(l0)
l1 = self.l1(f0)
f1 = flatten(l1)
self.loss = mean_squared_error(f1, t)
reporter.report({'loss': self.loss}, self)
return self.loss
def get_optimizer():
return Adam()
def training_main():
model = MyNet()
optimizer = get_optimizer()
optimizer.setup(model)
train, test = datasets.get_mnist(label_dtype=numpy.float32)
train_iter = chainer.iterators.SerialIterator(train, 50)
test_iter = chainer.iterators.SerialIterator(test, 50,
repeat=False,
shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer)
trainer = training.Trainer(updater, (10, 'epoch'))
trainer.extend(extensions.ProgressBar())
trainer.extend(extensions.Evaluator(test_iter, model))
trainer.extend(
extensions.PlotReport(['main/loss', 'validation/main/loss'],
'epoch'))
trainer.run()
if __name__ == '__main__':
training_main()
I followed the tutorial about Reading data with TF and made some tries myself. Now, the problem is that my tests show duplicate data in the batches I created when reading data from a CSV.
My code looks like this:
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import collections
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
class XICSDataSet:
def __init__(self, height=20, width=195, batch_size=1000, noutput=15):
self.depth = 1
self.height = height
self.width = width
self.batch_size = batch_size
self.noutput = noutput
def trainingset_files_reader(self, data_dir, nfiles):
fnames = [os.path.join(data_dir, "test%d"%i) for i in range(nfiles)]
filename_queue = tf.train.string_input_producer(fnames, shuffle=False)
reader = tf.TextLineReader()
key, value = reader.read(filename_queue)
record_defaults = [[.0],[.0],[.0],[.0],[.0]]
data_tuple = tf.decode_csv(value, record_defaults=record_defaults, field_delim = ' ')
features = tf.pack(data_tuple[:-self.noutput])
label = tf.pack(data_tuple[-self.noutput:])
depth_major = tf.reshape(features, [self.height, self.width, self.depth])
min_after_dequeue = 100
capacity = min_after_dequeue + 30 * self.batch_size
example_batch, label_batch = tf.train.shuffle_batch([depth_major, label], batch_size=self.batch_size, capacity=capacity,
min_after_dequeue=min_after_dequeue)
return example_batch, label_batch
with tf.Graph().as_default():
ds = XICSDataSet(2, 2, 3, 1)
im, lb = ds.trainingset_files_reader(filename, 1)
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
tf.train.start_queue_runners(sess=sess)
for i in range(1000):
lbs = sess.run([im, lb])[1]
_, nu = np.unique(lbs, return_counts=True)
if np.array_equal(nu, np.array([1, 1, 1])) == False:
print('Not unique elements found in a batch!')
print(lbs)
I tried with different batch sizes, different number of files, different values of capacity and min_after_dequeue, but I always get the problem. In the end, I would like to be able to read data from only one file, creating batches and shuffling the examples.
My files, created ad hoc for this test, have 5 lines each representing samples, and 5 columns. The last column is meant to be the label for that sample. These are just random numbers. I'm using only 10 files just to test this out.
The default behavior for tf.train.string_input_producer(fnames) is to produce an infinite number of copies of the elements in fnames. Therefore, since your tf.train.shuffle_batch() capacity is larger than the total number of elements in your input files (5 elements per file * 10 files = 50 elements), and the min_after_dequeue is also larger than the number of elements, the queue will contain at least two full copies of the input data before the first batch is produced. As a result, it is likely that some batches will contain duplicate data.
If you only want to process each example once, you can set an explicit num_epochs=1 when creating the tf.train.string_input_producer(). For example:
def trainingset_files_reader(self, data_dir, nfiles):
fnames = [os.path.join(data_dir, "test%d" % i) for i in range(nfiles)]
filename_queue = tf.train.string_input_producer(
fnames, shuffle=False, num_epochs=1)
# ...