def main(argv):
modelSpecs = config.InitializeModelSpecs()
modelSpecs = ParseCommandLine.ParseArguments(argv, modelSpecs)
## load the datasets. Data is a list of proteins and each protein is represented as a dict()
Data = DataProcessor.LoadDistanceLabelMatrices(modelSpecs['dataset'],
modelSpecs=modelSpecs)
print '#proteins loaded from the dataset: ', len(Data)
allProteins = [d['name'] for d in Data]
print 'Preparing batch data for training...'
groupSize = modelSpecs['minibatchSize']
batches = DataProcessor.SplitData2Batches(data=Data,
numDataPoints=groupSize,
modelSpecs=modelSpecs)
print "#batches:", len(batches)
## add code here to calculate empirical reference state
## RefState is a dict, RefState[response] = (length-independent ref, length-dependent ref)
## length-independent ref is an 1d array, length-dependent ref is a list with each element being an tuple (length, 1d array)
RefState = CalcRefState(batches=batches, modelSpecs=modelSpecs)
RefState['dataset'] = modelSpecs['dataset']
RefState['proteins'] = allProteins
## save RefState
responseStr = '-'.join(modelSpecs['responses'])
file4save = 'EmpRefState-' + responseStr + '-' + str(os.getpid()) + '.pkl'
fh = open(file4save, 'wb')
cPickle.dump(RefState, fh, protocol=cPickle.HIGHEST_PROTOCOL)
fh.close()
## print the length-ind reference state
for response in modelSpecs['responses']:
print RefState[response][0]
def main():
# learn embeddings
word2vec.word2vec()
# convert training,test and eval data into np arrays
DataProcessor.build_data()
# this calculates sentiments for the data
lstm.lstm_script()
def get_accuracy_windowed(classifier, parameters, length, slide, arms, wrists,
detections):
#Assemble training data
armIMU = arms[0]
wristIMU = wrists[0]
detection = detections[0]
for i in range(1, 5):
armIMU = np.concatenate((armIMU, arms[i]), axis=0)
wristIMU = np.concatenate((wristIMU, wrists[i]), axis=0)
detection = np.concatenate((detection, detections[i]), axis=0)
train_arm, train_wrist, train_detect = dp.apply_window(
length, slide, armIMU, wristIMU, detection)
print("Window applied to training data")
#Assemble validation data
arm, wrist, time, detect = dr.get_data(6)
detect = detect.astype(int)
detect = detect.ravel()
val_arm, val_wrist, val_detect = dp.apply_window(length, slide, arm, wrist,
detect)
print("Window applied to validation data")
train_arm_wrist = np.concatenate((train_arm, train_wrist), axis=1)
val_arm_wrist = np.concatenate((val_arm, val_wrist), axis=1)
accuracies = perform_classification(classifier, parameters, slide,
train_arm_wrist, train_detect,
val_arm_wrist, val_detect)
return accuracies
def CalcFeatureExpectBySampling(metaData, modelSpecs):
seqfeatures = []
seqweights = []
matrixfeatures = []
matrixweights = []
embedfeatures = []
embedweights = []
dataLocation = DataProcessor.SampleProteinInfo(metaData)
for loc in dataLocation:
d = DataProcessor.LoadRealData(loc, modelSpecs, loadLabel=False)
res = CalcFeatureExpect4OneProtein(d)
seqfeature, seqweight, matrixfeature, matrixweight = res[:4]
seqfeatures.append(seqfeature)
matrixfeatures.append(matrixfeature)
seqweights.append(seqweight)
matrixweights.append(matrixweight)
if len(res) == 6:
embedfeature, embedweight = res[5:]
embedfeatures.append(embedfeature)
embedweights.append(embedweight)
modelSpecs['seqFeatures_expected'] = np.average(seqfeatures,
axis=0,
weights=seqweights)
modelSpecs['matrixFeatures_expected'] = np.average(matrixfeatures,
axis=0,
weights=matrixweights)
modelSpecs['embedFeatures_expected'] = np.average(embedfeatures,
axis=0,
weights=embedweights)
def TrainDataLoader3(sharedQ, sharedLabelPool, sharedLabelWeightPool, stopTrainDataLoader, trainMetaData, modelSpecs, assembleData=True, UseSharedMemory=False): #print 'trainDataLoader has event: ', stopTrainDataLoader ## here we use labelPool to cache the labels of all the training proteins ## one protein may have multiple sets of input features due to MSA sampling or sequnence-template alignment ## but it can only have one set of label matrices, so it is worth to save all label matrices in RAM. labelPool = dict() labelWeightPool = dict() ## load the labels of all training proteins trainDataLocation = DataProcessor.SampleProteinInfo(trainMetaData) for loc in trainDataLocation: d = DataProcessor.LoadRealData(loc, modelSpecs, loadFeature=False, returnMode='list') name = d['name'] labelPool[name] = d['atomLabelMatrix'] labelWeightMatrix = LabelUtils.CalcLabelWeightMatrix(LabelMatrix=d['atomLabelMatrix'], modelSpecs=modelSpecs, floatType=np.float16) labelWeightPool[name] = labelWeightMatrix print 'TrainDataLoader with #PID ', os.getpid(), ' has loaded ', len(labelPool), ' label matrices and ', len(labelWeightPool), ' label weight matrices' ## update labelPool and labelWeightPool to the shared dict() sharedLabelPool.update(labelPool) sharedLabelWeightPool.update(labelWeightPool) print 'TrainDataLoader with #PID ', os.getpid(), ' has update the shared labelPool and labelWeightPool' while True: if stopTrainDataLoader.is_set() or os.getppid()==1: print 'trainDataLoader receives the stop signal' break trainDataLocation = DataProcessor.SampleProteinInfo(trainMetaData) numOriginals = len(trainDataLocation) """ maxLen = 900 trainDataLocation, numExcluded = DataProcessor.FilterByLength(trainDataLocation, maxLen) print 'Exclude ', numExcluded, ' train proteins longer than ', maxLen, ' AAs' """ trainSeqData = DataProcessor.SplitData2Batches(trainDataLocation, numDataPoints=modelSpecs['minibatchSize'], modelSpecs=modelSpecs) random.shuffle(trainSeqData) for batch in trainSeqData: if stopTrainDataLoader.is_set() or os.getppid()==1: print 'trainDataLoader receives the stop signal' break names = [ p['name'] for p in batch ] data = [] for protein in batch: d = DataProcessor.LoadRealData(protein, modelSpecs, loadLabel=False, returnMode='list') data.append(d) FeatureUtils.CheckModelNDataConsistency(modelSpecs, data) if assembleData: data = PrepareInput4Train(data, modelSpecs, floatType=np.float16, UseSharedMemory=UseSharedMemory) #print 'putting data to trainDataLoader queue...' sharedQ.put( (data, names) ) print 'TrainDataLoader has finished loading data' sharedQ.close()
def TrainByOneBatch(batch, train, modelSpecs, forRefState=False):
## batch is a list of protein locations, so we need to load the real data here
minibatch = DataProcessor.LoadRealData(batch, modelSpecs)
## add code here to make sure that the data has the same input dimension as the model specification
FeatureUtils.CheckModelNDataConsistency(modelSpecs, minibatch)
onebatch, names4onebatch = DataProcessor.AssembleOneBatch(
minibatch, modelSpecs, forRefState=forRefState)
x1d, x2d, x1dmask, x2dmask = onebatch[0:4]
## crop a large protein to deal with limited GPU memory. For sequential and embedding features, the theano model itself will crop based upon bounding box
bounds = SampleBoundingBox((x2d.shape[1], x2d.shape[2]),
modelSpecs['maxbatchSize'])
#x1d_new = x1d[:, bounds[1]:bounds[3], :]
x1d_new = x1d
x2d_new = x2d[:, bounds[0]:bounds[2], bounds[1]:bounds[3], :]
#x1dmask_new = x1dmask[:, bounds[1]:x1dmask.shape[1] ]
x1dmask_new = x1dmask
x2dmask_new = x2dmask[:, bounds[0]:x2dmask.shape[1], bounds[1]:bounds[3]]
input = [x1d_new, x2d_new, x1dmask_new, x2dmask_new]
## if embedding is used
##if any( k in modelSpecs['seq2matrixMode'] for k in ('SeqOnly', 'Seq+SS') ):
if config.EmbeddingUsed(modelSpecs):
embed = onebatch[4]
#embed_new = embed[:, bounds[1]:bounds[3], : ]
embed_new = embed
input.append(embed_new)
remainings = onebatch[5:]
else:
remainings = onebatch[4:]
##crop the ground truth and weight matrices
for x2d0 in remainings:
if len(x2d0.shape) == 3:
input.append(x2d0[:, bounds[0]:bounds[2], bounds[1]:bounds[3]])
else:
input.append(x2d0[:, bounds[0]:bounds[2], bounds[1]:bounds[3], :])
## add bounding box to the input list
input.append(bounds)
if config.TrainByRefLoss(modelSpecs):
if forRefState:
input.append(np.int32(-1))
else:
input.append(np.int32(1))
train_loss, train_errors, param_L2 = train(*input)
return train_loss, train_errors, param_L2
def icir(factor, r, n=20, rank=False):
if rank:
x1 = DP.standardize(rankdata(factor))
else:
x1 = DP.standardize(factor)
x2 = DP.standardize(r)
ic = (x1 * x2).mean(1).fillna(0)
ir = ic.rolling(20).mean() / ic.rolling(20).std()
return ic, ir
def DetermineFeatureDimensionBySampling(metaData, modelSpecs):
protein = DataProcessor.SampleProteinInfo(metaData, numSamples=1)[0]
d = DataProcessor.LoadRealData(protein, modelSpecs, loadLabel=False, returnMode='list')
## obtain the dimension of each type of input feature
modelSpecs['n_in_seq'] = DetermineNumSeqFeatures(d['seqFeatures'])
modelSpecs['n_in_matrix'] = DetermineNumMatrixFeatures(d['matrixFeatures']) + DetermineNumMatrixFeatures(d['matrixFeatures_nomean'])
if d.has_key('embedFeatures'):
modelSpecs['n_in_embed'] = d['embedFeatures'].shape[1]
def CalcLabelDistributionNWeightBySampling(trainMetaData, modelSpecs):
trainDataLocation = DataProcessor.SampleProteinInfo(trainMetaData,
numSamples=10000)
## only load ground truth but not input features to save memory and speed up
labelData = []
for loc in trainDataLocation:
p = DataProcessor.LoadRealData(loc, modelSpecs, loadFeature=False)
labelData.append(p)
CalcLabelDistributionAndWeight(labelData, modelSpecs)
def main():
q = input("enter a query to be processed> ")
while not q:
q = input("no empty queries please> ")
dp = DataProcessor()
# list_doc = dp.process_texts(sys.argv[1:])
reuters_texts = []
#Working with the first 50 files from the reuters library
reuters_data = reuters.fileids()[:200]
for data in reuters_data:
file_str = "" #concatinate file to string
file = reuters.open(data)
for line in file:
file_str = file_str + line
file_str = file_str.replace('\n','')
file_str = file_str.replace(" "," ")
file_str = file_str.replace(" ", " ")
reuters_texts.append(file_str)
# for text in reuters_texts:
# print(str(text)+"\n")
# #print(reuters_texts) # used for debugging purposes
[document_frequency, term_frequency_document] = dp.inverted_index(reuters_texts)
"""returns the document frequency and term frequency document
ITS A MUST WHEN CALCULATING THE TF-IDF
"""
term_weights = dp.compute_weights(term_frequency_document,reuters_texts)
# print the term weights
# for term,weights in term_weights.items():
# print(term," ",weights)
print("document_frequency: ", document_frequency)
[total_collection, total_distinct_terms] = dp.get_collection_lengths(reuters_texts)
[similarity,sorted_doc_list] = dp.bm25(reuters_texts,document_frequency,term_frequency_document,q)
document_lengths = dp.get_doc_length(reuters_texts)
query_likelyhood_scores = dp.query_likelyhood(reuters_texts,document_lengths,total_collection,total_distinct_terms,.5)
modded_query_vector = dp.rocchioAlgorithm(reuters_texts,term_weights,q,1,1,1)
precision_score = dp.precision(q,reuters_texts)
#output statements
#print("total_collection: ",total_collection)
#print("document lengths: " ,document_lengths)
print("Query: ",q)
print("using bm25 smoothing: ", similarity)
#print("sorted_doc_list: ",sorted_doc_list)
print("query_likelyhood_scores: ",query_likelyhood_scores)
print("modded_query_vector taken from Rocchios algorithm: ",modded_query_vector)
print("precision score from precision function for the query " + q + ": ", precision_score)
def ValidDataLoader2(sharedQ, stopValidDataLoader, validSeqData, modelSpecs, assembleData=True, UseSharedMemory=False):
bUseCCMFnorm, bUseCCMsum, bUseCCMraw, bUseFullMI, bUseFullCov = config.ParseExtraCCMmode(modelSpecs)
if any([bUseCCMraw, bUseFullMI, bUseFullCov]):
## when full coevolution matrices are used, we shall use float16 to save memory
floatType = np.float16
else:
floatType = theano.config.floatX
#print 'validDataLoader has event: ', stopValidDataLoader
for batch in validSeqData:
if stopValidDataLoader.is_set() or os.getppid()==1:
#print 'validDataLoader receives the stop signal'
break
## Load real data for one batch
data = DataProcessor.LoadRealData(batch, modelSpecs, returnMode='list')
## add code here to make sure that the data has the same input dimension as the model specification
FeatureUtils.CheckModelNDataConsistency(modelSpecs, data)
if assembleData:
data = PrepareInput4Validate(data, modelSpecs, floatType=floatType, UseSharedMemory=UseSharedMemory)
#print 'putting data to validDataLoader queue...'
sharedQ.put(data)
print 'validDataLoader has finished loading data'
sharedQ.close()
def ReplaceMissing(self,df: pd.DataFrame) -> pd.DataFrame:
#length = 3
#Create a dataprocessor object and convert the data in the csv and change all missing attribtues
Dp = DataProcessor.DataProcessor()
#Start the process to change the integrity of the dataframe from within the data processor
data = Dp.ReplaceMissingValue(df)
return data
def get_batch(batch_size):
batch = []
while (len(batch) < batch_size):
for op in operators: # for each operator...
data = []
img, inclasses = next(
iter(dataloaders[op]
)) # Gather a set of images and classes from them
for i in range(len(img)):
np = DataProcessor.get_neg_pairs([op, objects[inclasses[i]]],
operators, objects)
## Image, Object, Operator, nObject, nOperator
data.append([
Variable(feat_extractor(img[i].unsqueeze_(0))),
int(inclasses[i]),
int(operators.index(op)), np
])
if (len(data) == batch_size):
break
batch = batch + data
return [batch[i] for i in (random.sample(range(len(batch)), batch_size))]
def train_model_lstm(window_size=200, step_size=150):
data = dp.get_dataset_windowed(window_size, step_size)
model = create_baseline_lstm(data[0][0].shape)
history = model.fit(data[0],
data[1],
batch_size=64,
epochs=100,
validation_split=0.2,
verbose=0,
callbacks=callbacks)
model.save("model_lstm.h5")
file = open("results.txt", "a")
file.writelines([
'RNN results: \n', 'Topology: [LSTM(200),Dense(10)] \n',
'Accuracy: {} \n'.format(history.history['accuracy'][-1]),
'Val accuracy: {} \n'.format(history.history['val_accuracy'][-1]),
'Loss: {} \n'.format(history.history['loss'][-1]),
'Val loss: {} \n'.format(history.history['val_loss'][-1]), '\n'
])
file.close()
return model
def train_model_dnn():
data = dp.get_dataset_flat()
model = create_baseline_dnn()
history = model.fit(data[0],
data[1],
batch_size=64,
epochs=100,
validation_split=0.2,
verbose=0,
callbacks=callbacks)
model.save("model_dnn.h5")
file = open("results.txt", 'a')
file.writelines([
'DNN results: \n',
'Accuracy: {} \n'.format(history.history['accuracy'][-1]),
'Val accuracy: {} \n'.format(history.history['val_accuracy'][-1]),
'Loss: {} \n'.format(history.history['loss'][-1]),
'Val loss: {} \n'.format(history.history['val_loss'][-1]), '\n'
])
file.close()
return model
def compute_confusion_matrix(model):
data = dp.get_dataset_windowed(200, 150)
y_pred = model.predict(data[0])
cm = confusion_matrix(data[1].argmax(axis=1), y_pred.argmax(axis=1))
print(cm)
plt.matshow(cm, cmap='Blues')
plt.title("Confusion Matrix")
plt.ylabel("True label")
plt.xlabel("Predicted label")
classes = ["Walk", "Tölt", "Trot", "Canter"]
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes)
plt.yticks(tick_marks, classes)
thresh = cm.max() / 2
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
# if normalize:
# plt.text(j, i, "{:0.4f}".format(cm[i, j]),
# horizontalalignment="center",
# color="white" if cm[i, j] > thresh else "black")
# else:
plt.text(j,
i,
"{:,}".format(cm[i, j]),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.show()
res = model.evaluate(data[0], data[1])
print(res)
def init(inFile, outFile):
global MPIFX3, MPIHandler, MPIProcessor
global dev, handler, processor
MPIFX3 = mp.Queue()
MPIHandler = mp.Queue()
MPIProcessor = mp.Queue()
dev = FX3.Emulator(MPIFX3, inFile)
pipe = dev.getPipe()
buffSize = dev.getBufferSize()
handler = DataHandler.DataHandler(MPIHandler,
pipe,
buffSize,
filename=outFile)
realtime = handler.getRealtimeQueue()
handler.enableRealtime()
processor = DataProcessor.DataProcessor(MPIProcessor,
realtime, [[0, 2], [3, 3]],
legacy=False,
fs=2.5E6,
bufferSize=buffSize,
calcFlow=True,
numProcessors=2)
def Predict(self, newdata):
processed = []
processed.append(DataProcessor.Process(newdata[1]))
tfdata = self.tfidfconverter.Transform(processed)
pred = self.clf.predict(tfdata)
print(newdata, " ", pred)
print(" ")
def calc(datasetIndex, multiplierInt):
csv = pd.DataFrame(columns=['dataset', 'bins', 'f1', 'zero-one'])
exp = ((multiplierInt + 1) / 2)
bins = math.ceil(2**exp)
results = []
for k in range(trials):
dp = DataProcessor.DataProcessor(bin_count=bins)
binnedDataset = dp.StartProcess(datasets[datasetIndex])
N, Q, F, testData = train(binnedDataset)
model = Classifier.Classifier(N, Q, F)
classifiedData = model.classify(testData)
stats = Results.Results()
zeroOne = stats.ZeroOneLoss(classifiedData)
macroF1Average = stats.statsSummary(classifiedData)
datapoint = {
'dataset': dataset_names[datasetIndex],
'bins': bins,
'f1': macroF1Average,
'zero-one': zeroOne / 100
}
print(datapoint)
csv = csv.append(datapoint, ignore_index=True)
# trial = {"zeroOne": zeroOne, "F1": macroF1Average}
# results.append(trial)
# print(trial)
data.append(csv)
def generate_windowed_data(length, slide, fs):
train_arm, train_wrist, train_detect = dr.get_train_data()
train_arm = np.vstack(train_arm)
train_wrist = np.vstack(train_wrist)
train_detect = np.hstack(train_detect)
_, _, train_detect_window = dp.apply_window(length, slide, train_arm,
train_wrist, train_detect)
train_arm_window = da.apply_window_features(train_arm, length, slide, fs)
train_wrist_window = da.apply_window_features(train_wrist, length, slide,
fs)
print("Training: Full Arm Shape: " + str(np.shape(train_arm)))
print("Training: Full Wrist Shape: " + str(np.shape(train_wrist)))
print("Training: Full Detection Shape: " + str(np.shape(train_detect)))
print("Training: Windowed Arm Shape: " + str(np.shape(train_arm_window)))
print("Training: Windowed Wrist Shape: " +
str(np.shape(train_wrist_window)))
print("Training: Windowed Detect Shape: " +
str(np.shape(train_detect_window)))
val_arm, val_wrist, val_detect = dr.get_val_data()
val_arm = np.vstack(val_arm)
val_wrist = np.vstack(val_wrist)
val_detect = np.hstack(val_detect)
_, _, val_detect_window = dp.apply_window(length, slide, val_arm,
val_wrist, val_detect)
val_arm_window = da.apply_window_features(val_arm, length, slide, fs)
val_wrist_window = da.apply_window_features(val_wrist, length, slide, fs)
print("Validation: Windowed Arm Shape: " + str(np.shape(val_arm_window)))
print("Validation: Windowed Wrist Shape: " +
str(np.shape(val_wrist_window)))
print("Validation: Windowed Detect Shape: " +
str(np.shape(val_detect_window)))
data = {
"val_arm": val_arm_window,
"val_wrist": val_wrist_window,
"val_detect": val_detect_window,
"train_arm": train_arm_window,
"train_wrist": train_wrist_window,
"train_detect": train_detect_window
}
return data
def del_inventory(inventory_id, update_controls=[None]):
ip = dp.InventoryProcessor(database)
sql = ip.build_del_code(inventory_id=inventory_id)
ip.execute_sql_code(sql)
ip.db_con.commit()
ip.db_con.close()
if update_controls is not None:
IOProcessor.sel_inventory(update_controls[0])
def del_product(product_id, update_controls=[None]):
pp = DP.ProductsProcessor('Python210FinalDB.db')
sql = pp.build_del_code(product_id=product_id)
pp.execute_sql_code(sql)
pp.db_con.commit()
pp.db_con.close()
if update_controls is not None:
IOProcessor.sel_product(update_controls[0])
def del_product(product_id, update_controls=[None]):
pp = dp.ProductProcessor(database)
sql = pp.build_del_code(product_id=product_id)
pp.execute_sql_code(sql)
pp.db_con.commit()
pp.db_con.close()
if update_controls is not None:
IOProcessor.sel_product(update_controls[0])
def del_inventory_counts(inventory_id, update_controls=[None]):
pp = DP.InventoryCountProcessor('Python210FinalDB.db')
sql = pp.build_del_code(inventory_id=inventory_id)
pp.execute_sql_code(sql)
pp.db_con.commit()
pp.db_con.close()
if update_controls is not None:
IOProcessor.sel_inventorycounts(update_controls[0])
def RunIVSweep(sweepSpec):
KE2010.Initialize()
KE237.Initialize()
KE237.TurnOutputOn()
presentV = sweepSpec.startV
presentI = 0
VIList = []
while (presentV < sweepSpec.stopV):
print('presentV: ' + str(presentV))
KE237.SetVoltage(presentV)
presentI = KE2010.MeasureCurrent()
print('presentI: ' + str(presentI))
VIList.append((presentV, presentI))
presentV = presentV + sweepSpec.stepV
KE237.TurnOutputOff()
KE2010.GPIBReset()
dp.SaveListToCsv(VIList, name=HCPName)
dp.FitLineFromCsv(name=HCPName)
def SaveDataToFile():
try:
objF = DataProcessor.File()
objF.FileName = "EmployeeData.txt"
objF.TextData = Employees.EmployeeList.ToString()
print("Reached here")
objF.SaveData()
except Exception as e:
print(e)
def SaveDataToFile():
try:
objF = DataProcessor.File()
objF.FileName = "CustomerData.txt"
objF.TextData = Customers.CustomerList.ToString()
print("Reached here")
objF.SaveData()
except Exception as e:
print(e)
def get_attention_weights(data, embds):
tf.reset_default_graph()
it = 0
now = "han_100d_163b_50cx_0.0001_0.5d"
with tf.Session() as sess:
model = HierarchicalAttention(
num_classes=2,
vocab_size=embds.shape[0],
embedding_size=embds.shape[1]
)
root_logdir = "logs"
logdir = "{}/run-{}-{}/".format(root_logdir, now, it)
checkpoint_dir = "{}checkpoints".format(logdir)
saver = tf.train.Saver()
# saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_dir))
# Training model
# training_op, global_step = model.optimize()
sess.run(tf.global_variables_initializer())
sess.run(model.embedding_init, feed_dict={model.embedding_placeholder: embds})
saver.restore(sess, checkpoint_dir)
predictions = model.predict()
# print("Evaluation:")
x_val, y_val, sent_lengths_val, seq_lengths_val = data.fetch_val()
feed_dict = {model.x: x_val, model.y: y_val, model.sent_lengths: sent_lengths_val,
model.seq_lengths: seq_lengths_val, model.dropout_keep_prob: 1,
model.max_seq_length: data.val_max_seq_length,
model.max_sent_length: data.val_max_sent_length
}
pred, a_word, a_sent = sess.run([predictions, model.alphas_word, model.alphas_sent], feed_dict=feed_dict)
#pred, a1, A = sess.run([predictions, model.alphas1, model.alphas2, model.alphas3, model.alphas4],
#feed_dict=feed_dict)
a_word = np.reshape(a_word, [-1, data.val_max_seq_length, data.val_max_sent_length, 1])
# filter on correct predictions
zipped = list(zip(x_val, pred['labels'], pred['predictions'], pred['probabilities'], a_word, a_sent))
# print(zipped[0:2])
selection = [list(x) for x in zipped][133]
zipped_correct = [list(x) for x in zipped if x[1]==x[2] and x[1] == 1]
# print(zipped_correct[0:2])
def get_predicted_prob(x):
return (x[3])[(x[2])]
sorted_correct = sorted(zipped_correct, key=get_predicted_prob, reverse=True)
print(sorted_correct[0:2])
#selection = sorted_correct[1]
selection_zipped_tuple = list(zip(selection[0], selection[4], selection[5]))
#selection_zipped_tuple = list(zip(selection[0], selection[4]))
selection_zipped = [list(x) for x in selection_zipped_tuple]
for s in selection_zipped:
s[0] = dp.translate_to_voc(s[0])
return selection_zipped
def upd_inventory(inventory_id, inventory_date, update_controls=[None]):
pp = DP.InventoryProcessor('Python210FinalDB.db')
sql = pp.build_upd_code(inventory_id=inventory_id,
inventory_date=inventory_date)
pp.execute_sql_code(sql)
pp.db_con.commit()
pp.db_con.close()
if update_controls is not None:
IOProcessor.sel_inventory(update_controls[0])
def SaveDataToFile():
#Save data to file
try:
objF = DataProcessor.File()
objF.FileName = "CustomerData.txt"
objF.TextData = Customers.CustomerList.ToString()
objF.SaveData()
except Exception as e:
print(e)
import tables as tab
import numpy as np
import DataProcessor as dp
datafile = tab.openFile('InstrumentedBicycleData.h5')
datatable = datafile.root.data.datatable
nanList = []
for x in datatable.iterrows():
cell = x['AccelerationX']
vnSampRate = x['NINumSamples']
vnSig = dp.unsize_vector(cell, vnSampRate)
numNan = np.sum(np.isnan(vnSig))
if numNan > 2:
nanList.append((x['RunID'], numNan))
nanList.sort(key=lambda x: x[1])
for thing in nanList:
print thing
import numpy as np
import cv2
import pickle
import DataProcessor
import EmotionLearner
from sklearn import svm
import matplotlib.pyplot as plt
dataProcessor=DataProcessor()
data,label=dataProcessor.loadCKData_With_Hog_32x32_3x3()
data,featureMeans,featureVariance=dataProcessor.normalizeData(data)
emotionLearner=EmotionLearner()
scores=emotionLearner.crossValidateSVM(data,label);
print scores
print np.means(scores)
clf=emotionLearner.trainSVM(data,label);
#save data and svm
with open('classifier/trainingDataCK.pkl', 'wb') as output:
pickle.dump(data, output, pickle.HIGHEST_PROTOCOL)
pickle.dump(label, output, pickle.HIGHEST_PROTOCOL)
pickle.dump(featureMeans, output, pickle.HIGHEST_PROTOCOL)
pickle.dump(featureVariance, output, pickle.HIGHEST_PROTOCOL)
with open('classifier/svmCK.pkl', 'wb') as output:
pickle.dump(clf, output, pickle.HIGHEST_PROTOCOL)
# for index, data_frame in zip(range(len(data_frames)), data_frames):
# plot([d.daily_return(data_frame)], 'Daily Return', market_name[index].upper()+' Index')
# print(data_frames[0].head(5))
#d.plot_data([data_frames[0],data_frames[7]], ['Daily Return'], market_names=market_name)
#plt.show()
# Plot data
# data_frames[0]['Daily Return'].plot()
#print(data_frames[0].index.name)
#print(data_frames[0].columns.values.tolist())
dp = DataProcessor()
data_points = [4, 8, 12]
# Compute moving average
for data_frame in data_frames:
data_frame = dp.get_moving_average(data_frame, data_points)
#Compute exponential moving average
for data_frame in data_frames:
data_frame = dp.get_ewma(data_frame, data_points)
# cols=['Adj Close','MA_5','MA_10','MA_15','MA_20']
# plot different calculations
'''
import matplotlib.pyplot as plt
from scipy import stats
from scipy.interpolate import UnivariateSpline
import DataProcessor as dp
# pick a run number
runid = 124
print "RunID:", runid
# open the data file
datafile = tab.openFile('InstrumentedBicycleData.h5')
datatable = datafile.root.data.datatable
# get the raw data
niAcc = dp.get_cell(datatable, 'FrameAccelY', runid)
vnAcc = dp.get_cell(datatable, 'AccelerationZ', runid)
sampleRate = dp.get_cell(datatable, 'NISampleRate', runid)
numSamples = dp.get_cell(datatable, 'NINumSamples', runid)
speed = dp.get_cell(datatable, 'Speed', runid)
threeVolts = dp.get_cell(datatable, 'ThreeVolts', runid)
# close the file
datafile.close()
# make a nice time vector
time = dp.time_vector(numSamples, sampleRate)
# scale the NI signal from volts to m/s**2, and switch the sign
niSig = -(niAcc - threeVolts / 2.) / (300. / 1000.) * 9.81
vnSig = vnAcc
import tables as tab
import numpy as np
import matplotlib.pyplot as plt
import DataProcessor as dp
datafile = tab.openFile('InstrumentedBicycleData.h5')
datatable = datafile.root.data.datatable
for x in datatable.iterrows():
if x['RunID'] == 4:
pass
else:
if x['Maneuver'] != 'System Test':
numSamp = x['NINumSamples']
sampleRate = x['NISampleRate']
time = np.linspace(0., numSamp/sampleRate, num=numSamp)
acceleration = dp.unsize_vector(x['FrameAccelY'], numSamp)
print '--------------------'
print 'Run ID:', x['RunID']
print 'Speed:', x['Speed']
print 'Notes:', x['Notes']
print 'Environment:', x['Environment']
print 'Maneuver:', x['Maneuver']
print 'Total time:', time[-1]
print 'Time of max value:', time[np.argmax(acceleration)]
print 'Max value:', np.max(acceleration)
print '--------------------'
if time[np.argmax(acceleration)] > 5.:
plt.figure(x['RunID'])
plt.plot(time, acceleration)
plt.title(x['Speed'])
week = datetime.timedelta(days = 7)
days = Analysis.findHistoricalWeekdays(data, specifiedDate)
# extends to 1 month behind and ahead of these days
# grabs [-3, 0, 3], then adds [-4, -1, 2] and [-2, 1, 4]
days = map(lambda x: x - 3 * week, days) + days + map(lambda x: x + 3 * week, days)
days = map(lambda x: x - 1 * week, days) + days + map(lambda x: x + 1 * week, days)
# return only the ones in the dataset
return [day for day in days if (data.has_key(day))]
if __name__ == "__main__":
formatter = "{0:.2f}"
print("Processing file " + sys.argv[1] + " for " + sys.argv[2])
dataMap = DataProcessor.processFile(sys.argv[1])
# find the rational uper bound on graphs
dataMax = 0
for values in dataMap.values():
p = np.percentile(values, 90)
dataMax = max(dataMax, p)
givenDate = sys.argv[2].split("-")
givenDate = datetime.date(int(givenDate[0]), int(givenDate[1]), int(givenDate[2]))
givenTime = None
if (len(sys.argv) >= 4):
givenTime = sys.argv[3].split(":")
givenTime = datetime.time(int(givenTime[0]), int(givenTime[1]), int(givenTime[2]))
# grabs the filepath
# Setting up all the options!
opts_prepro = {'rescale':rescale, 'scaleFactor':scaleFactor, 'colorSpace':colorSpace}
opts_seeds = {'method':seedSelection}
opts_labeller = {'labelsType':labelsType, 'labelThrs':labelThrs, 'colorSpace':colorSpace}
#############################################
# 2. Now it's time to play with the data
#############################################
gt_index = 0
TLabels = []
for im in Images:
#############################################
# 2.1. Data preprocessing & transformation
#############################################
imprep = dp.processData(im, opts_prepro)
#############################################
# 2.2. Color space transformation (from Image to np.ndarray X)
#############################################
X = ce.transformColorSpace(imprep, opts_prepro)
#############################################
# 2.3. Selection of K (number of clusters)
#############################################
#Done already below for K == -1
#############################################
# 2.4. Selection of the K seeds --> Seeds
#############################################
if K == -1:
resultList = []
if normalizedQUarterlyReviewCount[i-1][1] == 0:
quarterDeltas.append([normalizedQUarterlyReviewCount[i][0], 0])
else:
quarterDeltas.append([normalizedQUarterlyReviewCount[i][0], (normalizedQUarterlyReviewCount[i][1]-normalizedQUarterlyReviewCount[i-1][1])/normalizedQUarterlyReviewCount[i-1][1]])
return quarterDeltas
if __name__ == '__main__':
ticker = "SBUX";
bizFullName = "Starbucks"
conn = mysql.connector.connect(user='******', password='******',
host='107.170.18.102',
database='FinanceNLP')
cursor_select = conn.cursor()
erDates = getERDates(ticker, cursor_select)#read ER dates from my DB
dailyReviewCounts = DataProcessor.getDailyReviewsCount(bizFullName)#list of tuple(date, review count)
quarterReviewCounts = getQuarterCumulated(erDates, dailyReviewCounts)#review count over quarters, tuple list (ER date, previous quarter review count)
quarterReviewCountsList = [[item[0], item[1]] for item in quarterReviewCounts.items()]
quarterReviewCountsList.sort(key = operator.itemgetter(0), reverse=False)
normalizedQuarterlyReviewCount = getNormalizedQuarterlyReviewCount(quarterReviewCountsList)#TODY: other normalized method? or no normalize
quarterDeltas = getQuarterlyReviewCountDeltas(normalizedQuarterlyReviewCount)#difference between quarters
# now quarterDeltasList becomes daily average review count in each quarter
ER2Surprise = getStockPriceSurprise(ticker, erDates)#hash (date, price change)
# print ER2Surprise
lineReviewCount = []
linePrice = []
lineER = []
print "Date\t", "ReviewCount Change\t", "Price Change\n",
for er in quarterDeltas:
dt = er[0]
import tensorflow as tf
import numpy as np
import DataProcessor as dp
from matplotlib import pyplot
nodeId = 205
source = "House"
startDate = "2016-01-12"
endDate = "2016-01-12"
feature_columns = [tf.feature_column.numeric_column("x", shape=[9])]
[dataSetTime,dataSetTemp,trainingDataX,trainingDataY,testDataX,testDataY]=dp.getParams(nodeId,source,startDate,endDate)
estimator = tf.estimator.DNNRegressor(
feature_columns=feature_columns,
hidden_units=[30, 30, 30],
optimizer=tf.train.ProximalAdagradOptimizer(
learning_rate=10,
l1_regularization_strength=0.001
))
cX = 0
inp = trainingDataX
batchSize = 500
numBatches = len(trainingDataX)//batchSize
def input_fn():
global cX
if(cX > len(trainingDataX)):
