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routes.py
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routes.py
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#
# main.py
# Jonathan Pilault, 2016-08-01
# Copyright (c) 2016 mldb.ai inc. All rights reserved.
#
import json
import re
import math
import main as mv
#mldb.log("Routes is called")
mldb = mldb_wrapper.wrap(mldb)
rp = mldb.plugin.rest_params
########################
# Variable definitions #
########################
# User defined
newMax = 1
newMin = 0
rad_train = 1.9
rad_user_img = 1.9
alpha_train = 10
alpha_user_img = 10
save_drawing = False
# Only change in models/main.py
mode = mv.get_mode()
allowable_models = mv.get_models()
unique_labels = mv.get_allowed_labels()
base_model_url = "file://" + mldb.plugin.get_plugin_dir() + "/models/"
# No need to change
prob_scale = 100
explain_scale = 10
max_pixel_value = 255
min_pixel_value = 0
no_feat_drawing = 784
sq = 28 # square root of no_feat_drawing
dataset_url = "///mldb_data/datasets/"
save_name = "user_drawing"
final_name = "user_drawing_final"
final_dataset_name = "digits_raw" # digits_trans
########################
# Function definitions #
########################
def is_empty(anyDataStructure):
if (anyDataStructure): return False
else: return True
def centerNorm(row, mean, std):
xms = zip(row, mean, std)
new_row = map(lambda (x, m, s): (x-m) / s if abs(s)>0.001 else (x-m), xms)
return new_row
def change_range(row, oldMax, oldMin, newMax, newMin):
oldRange = oldMax - oldMin
newRange = newMax - newMin
new_row = map(lambda x: (x-oldMin) * newRange / oldRange + newMin, row)
return new_row
def make_black_white(row):
new_row = map(lambda x: 1 if abs(x)>0.1 else 0, row)
return new_row
def create_convolution():
JsConvolutionExpr = """
jseval('
var row_val = val;
var radius = rad;
var alpha = alpha_;
// input 1D list, output 1D list, width, height, alpha
function laplaceSharpen(inList, outList, w, h, alpha) {
var weights = [1, 1, 1, 1, -8, 1, 1, 1, 1]; // sharpen convolution matrix
var rs = 3; // kind of similar to radius -- needs to be sqrt(weights.length)
for (var i = 0; i < h; i++)
for (var j = 0; j < w; j++) {
var val = 0;
var indexW = 0;
for (var iy = i; iy < i + rs; iy++)
for (var ix = j; ix < j + rs; ix++) {
var x = Math.min(w - 1, Math.max(0, ix));
var y = Math.min(h - 1, Math.max(0, iy));
val += inList[y * w + x] * weights[indexW];
indexW ++;
}
new_value = inList[i * w + j] - val * alpha;
outList[i * w + j] = new_value;
}
return outList;
} // End of laplaceSharpen
// input 1D list, output 1D list, width, height, radius
function gaussianBlur(inList, outList, w, h, r) {
var rs = Math.ceil(r * 2.57); // index around pixel must be int
for (var i = 0; i < h; i++)
for (var j = 0; j < w; j++) {
var val = 0,
wsum = 0;
for (var iy = i - rs; iy < i + rs + 1; iy++)
for (var ix = j - rs; ix < j + rs + 1; ix++) {
var x = Math.min(w - 1, Math.max(0, ix));
var y = Math.min(h - 1, Math.max(0, iy));
var dsq = (ix - j) * (ix - j) + (iy - i) * (iy - i);
var wght = Math.exp(-dsq / (2 * r * r)) / (Math.PI * 2 * r * r);
val += inList[y * w + x] * wght;
wsum += wght;
}
outList[i * w + j] = val / wsum;
}
return outList;
} // End of gaussianBlur
//Assuring that the 1d row is in the right order
var length = row_val.length;
var dim = Math.sqrt(length);
var matrix = new Array(length);
for (var i = 0; i < length; i++) {
matrix[row_val[i][0][0]] = row_val[i][1];
}
//Using functions
var blurredMatrix = [];
var NoisyMatrix = [];
blurredMatrix = gaussianBlur(matrix, blurredMatrix, dim, dim, radius);
NoisyMatrix = laplaceSharpen(blurredMatrix, NoisyMatrix, dim, dim, alpha);
return NoisyMatrix;
','val, rad, alpha_', valueExpr, radius, alpha
) AS *
"""
print mldb.put("/v1/functions/convolution", {
"type": "sql.expression",
"params": {
"expression": JsConvolutionExpr,
"prepared": True
}
})
def get_explain_and_prob(valueExpr, radius, alpha, unique_labels, procedureRunName,
no_feat_drawing):
probExpr = ""
explainExpr = ""
explains = []
probabilities = []
for i in unique_labels:
probExpr = probExpr + \
"probabilizer_%(procedureRunName)s_%(i)d({score: scores.\"%(i)d\"})[prob] as prob_%(i)d, " \
%{"procedureRunName": procedureRunName,
"i" : i}
explainExpr = explainExpr + \
"explain_%(procedureRunName)s({features: %(valueExpr)s, label: %(i)d})[explanation] AS explain_%(i)d, " \
%{"procedureRunName": procedureRunName,
"i" : i,
"valueExpr": valueExpr
}
SQL_Expr = """
SELECT
%(probExpr)s
explain*
FROM (
SELECT
%(explainExpr)s
%(procedureRunName)s_scorer_0({features: %(valueExpr)s})[scores] AS scores
)
""" % {
"probExpr": probExpr,
"explainExpr": explainExpr,
"procedureRunName": procedureRunName,
"valueExpr": valueExpr,
"radius": radius,
"alpha": alpha
}
queryDump = mldb.query(SQL_Expr)
#mldb.log(queryDump)
target = -1
for resultHeaders, results in zip(queryDump[0], queryDump[1]):
explainMatch = re.match('(?:explain_)(_*.)(_*.)(.*.)', resultHeaders)
probMatch = re.match('(?:prob_)(_*.)', resultHeaders)
if (explainMatch != None):
current_target = int(explainMatch.group(1))
index = int(explainMatch.group(3))
if current_target != target:
explains.append([0] * no_feat_drawing)
target = current_target
explains[current_target][index] = float(results) * explain_scale
#mldb.log(explain[index])
#mldb.log(explains[current_target][index])
elif (probMatch != None):
probabilities.append(float(results) * prob_scale)
return (explains, probabilities)
def save_to_csv(drawing_features, no_feat_drawing, label_names, label_values,
path, save_name):
import csv
import os
file_name = path + save_name + ".csv"
try:
os.makedirs(path)
except OSError:
if not os.path.isdir(path):
raise
with open(file_name, 'wb') as ds:
wr = csv.writer(ds, quoting=csv.QUOTE_ALL)
header = [str(i) for i in range(no_feat_drawing)]
if (label_names != None):
header.extend(label_names)
wr.writerow(header)
if (label_values != None):
for feature, label_value in zip(drawing_features, label_values):
feature.extend(label_value)
wr.writerow(feature)
else:
wr.writerow(drawing_features)
conf = {
"type":"import.text",
"params": {
"dataFileUrl": "file:" + file_name,
"outputDataset": {
"id": "%s" %save_name,
"type": "tabular"
},
"runOnCreation": True
}
}
create_drawingSave = mldb.put("/v1/procedures/%s" % save_name, conf)
mldb.log(create_drawingSave)
def addNoise(data_name, final_dataset_name, labelNeeded, radius, alpha):
# This steps does the equivalent of gaussianBlur and laplaceSharpen
# It is faster since it uses MLDB's parallel processing capabilities
mldb.log("data_name: %s" %data_name)
mldb.log("final_dataset_name: %s" %final_dataset_name)
mldb.log("labelNeeded: %s" %labelNeeded)
mldb.log("radius: %s" %radius)
mldb.log("alpha: %s" %alpha)
valueExpr = "{*}"
labelsExpr = ""
if labelNeeded: # Labels needed in training/testing data creation step
valueExpr = "{* EXCLUDING(label*)}"
labelsExpr = ",label*"
SQL_Expr = """
SELECT convolution({valueExpr: %(valueExpr)s, radius: %(radius)d,
alpha: %(alpha)d}) AS *
%(labelsExpr)s
FROM %(datasetName)s
""" % {
"valueExpr": valueExpr,
"radius": radius,
"alpha": alpha,
"labelsExpr": labelsExpr,
"datasetName": data_name
}
conf = {
"type": "transform",
"params": {
"inputData": SQL_Expr,
"outputDataset": {
"id": "%s" %final_dataset_name,
"type": "tabular"
},
"runOnCreation": True
}
}
create_noisy_data = mldb.put('/v1/procedures/%s' % final_dataset_name, conf)
mldb.log(create_noisy_data)
###############################
###### Payload handling #######
###############################
if rp.verb == "GET" and rp.remaining == "/mnist_pics":
import random
import png
labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
#pic_array = []
colors = [
[0, 0, 0, 0],
[0, 0, 0, 128], # [0, 0, 255, 128] for transparent bleu
]
no_examples = 11000
for label in labels:
offset = random.randint(0, no_examples/len(labels))
x_data = mldb.query("""
SELECT * EXCLUDING(label)
FROM digits_mnist
WHERE label = %d
LIMIT 1
OFFSET %d
""" %(label, offset))
pixels = make_black_white(row = x_data[1][1:]) # pixels here is either 0 or 1
pixels = sum(map(lambda x: colors[x], pixels), []) # pixels is converted in colors
file_name = mldb.plugin.get_plugin_dir() + "/static/%d.png" %label
f = open(file_name, 'wb') # binary mode is important
w = png.Writer(width=sq, height=sq, alpha=True)
w.write_array(f, pixels)
f.close()
mldb.plugin.set_return("");
###############################
###### Payload handling #######
###############################
if rp.verb == "POST" and rp.remaining == "/handle_drawing":
########################
# Input payload info #
########################
payload = json.loads(rp.payload)
model = payload['procedure']
drawing_features = payload['user_input']
########################
# Setting up variables #
########################
accuracy = None
explainString = None
explains = []
probabilities = []
modelFileUrlPattern = base_model_url + model
if (model not in allowable_models):
mldb.log("Chosen procedure was not created yet!!")
else:
drawing_features = change_range(row = drawing_features, oldMax = max_pixel_value,
oldMin = min_pixel_value, newMax = newMax, newMin = newMin)
if save_drawing:
save_to_csv(drawing_features = drawing_features, no_feat_drawing = no_feat_drawing,
label_names = None, label_values = None,
path=dataset_url, save_name=save_name)
_model = "_" + model
_mode = "_" + mode
procedureRunName = "mnist" + _model + _mode
create_convolution()
explains, probabilities = get_explain_and_prob(valueExpr = drawing_features, radius = rad_user_img,
alpha = alpha_user_img, unique_labels = unique_labels, procedureRunName = procedureRunName,
no_feat_drawing = no_feat_drawing)
output_ = {
"accuracy": accuracy,
"explainString": explainString,
"explains": explains,
"scores": probabilities
}
mldb.plugin.set_return(output_);
#######################################
############ DATA CREATION ############
#######################################
if rp.remaining == "/create_data":
########################
# Variable definitions #
########################
# Do not change
mnist_data_url = 'http://deeplearning.net/data/mnist/mnist.pkl.gz'
# User defined
data_name = "digits_raw"
label_name = "label"
mldb.log(mldb.plugin.get_plugin_dir())
mldb.log("Main.py is loaded to create the MNIST dataset...")
import cPickle, gzip
from StringIO import StringIO
from urllib import urlopen
import numpy as np
sets = []
# Load the data
try:
inmemory = StringIO(urlopen(mnist_data_url).read())
fStream = gzip.GzipFile(fileobj=inmemory, mode='rb')
sets = list(cPickle.load(fStream)) #tupple has 3 sets: train, validation and test sets
fStream.close()
except Exception as xcpt:
mldb.log("Failed to load file %s" % xcpt)
if (is_empty(sets)):
pass
else:
mldb.delete("/v1/datasets/%s" % data_name)
preprocessedData = []
labelNames = []
labelData = []
index = 0
mean = 0
std = 1
for i, set_ in enumerate(sets):
if (i == 0):
labelNames.append(label_name)
for target in enumerate(unique_labels):
labelNames.append(label_name + "_" + str(target[1]))
#mean = np.mean(set_[0], axis=0) # doing this only for train set is good enough
#std = np.std(set_[0], axis=0) # doing this only for train set is good enough
# each set is composed of a feature matrix and labels
data = set_[0].tolist()
label = set_[1].tolist()
for j, row in enumerate(data):
###################
# Preprocess data #
###################
# Zero-centering and normalize data
#row = centerNorm(row = row, mean = mean, std = std)
#oldMax = np.amax(row)
#oldMin = np.amin(row)
# Change range --> i.e. linear models don't like zero values
#row = change_range(row = row, oldMax = oldMax, oldMin = oldMin,
#newMax = newMax, newMin = newMin)
row = make_black_white(row = row)
labelRow = [label[j]]
# One hot vectors for each label:
for target in enumerate(unique_labels):
bool_value = 0
if target[1] == label[j]: bool_value = 1
labelRow.append(bool_value)
# appending newest row
preprocessedData.append(row)
labelData.append(labelRow)
# End for loop
index = index + 1
# End for loop
# Creatng a dataset via a csv MLDB text import procedure
save_to_csv(drawing_features = preprocessedData, no_feat_drawing = no_feat_drawing,
label_names = labelNames, label_values = labelData,
path = dataset_url, save_name = data_name)
mldb.log("Saved to CSV and initial dataset created.")
# creating convolution function to use in the next step
#create_convolution()
# Adding noise by blurring and sharpening images
#addNoise(data_name = data_name, final_dataset_name = final_dataset_name,
#labelNeeded = True, radius = rad_train, alpha = alpha_train)
#mldb.log("Initial dataset transformed with added noise.")
mldb.plugin.set_return("Success in creating data");
########################################
############ MODEL CREATION ############
########################################
if rp.remaining == "/train_models":
data_name = final_dataset_name
label_name = "label"
mldb.log("Main.py is loaded to train classifier and probabilizer models...")
# Configuration dictionary:
bbdt_d5_config = {
"bbdt_d5": {
"type": "bagging",
"verbosity": 3,
"weak_learner": {
"type": "boosting",
"verbosity": 3,
"weak_learner": {
"type": "decision_tree",
"max_depth": 7,
"verbosity": 0,
"update_alg": "gentle",
"random_feature_propn": 0.3
},
"min_iter": 5,
"max_iter": 30
},
"num_bags": 5
}
}
dt_config = {
"dt": {
"type": "decision_tree",
"max_depth": 14,
"verbosity": 3,
"update_alg": "prob"
,"random_feature_propn": 0.8
}
}
glz_config = {
"glz": {
"type": "glz",
"verbosity": 3,
"normalize": True,
"link_function": "logit",
"regularization": "l2"
}
}
bglz_config = {
"bglz": {
"_note": "Bagged random GLZ",
"type": "bagging",
"verbosity": 1,
"validation_split": 0.1,
"weak_learner": {
"type": "glz",
"feature_proportion": 1.0,
"link_function": "logit",
"regularization": "l2",
"verbosity": 0
},
"num_bags": 6
}
}
bbs2_config = {
"bbs2": {
"_note": "Bagged boosted stumps",
"type": "bagging",
"num_bags": 5,
"weak_learner": {
"type": "boosting",
"verbosity": 3,
"weak_learner": {
"type": "decision_tree",
"max_depth": 1,
"verbosity": 0,
"update_alg": "gentle"
},
"min_iter": 5,
"max_iter": 300,
"trace_training_acc": "true"
}
}
}
configs_dct = {
"bbdt_d5": bbdt_d5_config,
"dt": dt_config,
"glz": glz_config,
"bglz": bglz_config,
"bbs2": bbs2_config
}
for model in allowable_models:
config = configs_dct[model]
modelFileUrlPattern = base_model_url + model
run_again = True
for target in unique_labels:
_model = "_" + model
_mode = "_" + mode
procedureRunName = "mnist" + _model + _mode
if (mode == "categorical"):
_target = ""
if (mode == "boolean"):
_target = "_" + str(target)
run_again = True # "boolean" runs for each target while categorical runs only once
if (run_again):
# classifier model
a = mldb.delete("/v1/procedures/%s" % (procedureRunName + _target))
a_f = mldb.delete("/v1/functions/%s" % (procedureRunName + _target))
a_s = mldb.delete("/v1/functions/%s_scorer_0" % (procedureRunName + _target))
mldb.log("DELETE classifier")
mldb.log(a)
conf_class = {
"type": "classifier.experiment",
"params": {
"experimentName": (procedureRunName + _target),
"mode": mode,
"inputData" : """
select
{* EXCLUDING(label*)} AS features,
label%s AS label
from %s
""" % (_target, data_name),
"datasetFolds": [
{
"trainingWhere": "rowHash() % 5 != 0",
"testingWhere": "rowHash() % 5 = 0"
#,"trainingLimit": 500,
#"testingLimit": 50
}],
"algorithm": model,
"configuration": config,
"modelFileUrlPattern": "%s/%s.cls" % (modelFileUrlPattern, (procedureRunName + _target)),
"keepArtifacts": True,
"outputAccuracyDataset": True,
"runOnCreation": True,
"evalTrain": True
}
}
mldb.log(" TRAINING %s CLASSIFIER **********************" % model)
b = mldb.put("/v1/procedures/%s" % (procedureRunName + _target), conf_class)
mldb.log("PUT classifier")
mldb.log(b)
run_again = False # "boolean" runs for each target while categorical runs only once
_target_prob = "_" + str(target)
# probabilizer model
c = mldb.delete("/v1/procedures/probabilizer_%s" % (procedureRunName + _target_prob))
mldb.log("DELETE probabilizer")
mldb.log(c)
conf_prob = {
"type": "probabilizer.train",
"params": {
"trainingData":{
"select": """
%(path)s_scorer_0({ features: {* EXCLUDING(label*)} })[scores.\"%(target)s\"] AS score,
label%(_target_prob)s AS label
"""
% {"path":(procedureRunName + _target), "target": str(target), "_target_prob":_target_prob},
"from": {"id": data_name},
"where": "rowHash() % 5 = 0" # this should be trained on the test set
},
"modelFileUrl": "%s/probabilizer_%s.cls" % (modelFileUrlPattern, (procedureRunName + _target_prob)),
"functionName": "probabilizer_%s" % (procedureRunName + _target_prob),
"runOnCreation": True
}
}
mldb.log(" TRAINING %s PROBABILIZER **********************" % model)
d = mldb.put("/v1/procedures/probabilizer_%s" % (procedureRunName + _target_prob), conf_prob)
mldb.log("PUT probabilizer")
mldb.log(d)
mldb.plugin.set_return("Success in training models");