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lint remove unused imports #3

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291 changes: 170 additions & 121 deletions Conv_LSTM_CAE/ConvAE.py
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245 changes: 103 additions & 142 deletions Conv_LSTM_CAE/Convolutional_Autoencoder_Classifier_CNV.py
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Original file line number Diff line number Diff line change
@@ -1,128 +1,80 @@
# coding: utf-8
from time import time
import pandas as pd
import numpy as np
np.random.seed(10)

import keras
import keras.backend as K
K.set_image_dim_ordering("tf")

import numpy as np
import gzip
from keras.models import Model
from keras.optimizers import RMSprop
from keras.layers import Input,Dense,Flatten,Dropout,merge,Reshape,Conv2D,MaxPooling2D,UpSampling2D,Conv2DTranspose
from keras.layers.normalization import BatchNormalization
from keras.models import Model,Sequential
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adadelta, RMSprop,SGD,Adam
from keras import regularizers
from keras import backend as K
from keras.models import Model, Sequential
from keras.optimizers import Adadelta, RMSprop, SGD, Adam
from keras.regularizers import L1L2
from keras.utils import to_categorical
from keras.layers import (
Input,
Dense,
LSTM,
Dense,
Dropout,
Activation,
Flatten,
BatchNormalization,
Conv1D,
Conv2D,
MaxPooling2D,
Convolution2D,
UpSampling1D,
MaxPooling1D,
GlobalMaxPooling1D,
GlobalAveragePooling1D,
)

import os
import pandas as pd
import numpy as np
import sys
from time import time

from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn import metrics

from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout, Activation, Flatten, BatchNormalization
from keras.layers import TimeDistributed
from keras.callbacks import TensorBoard
from keras.optimizers import RMSprop
from keras.regularizers import l2
from keras.callbacks import EarlyStopping
from sklearn.metrics import precision_recall_fscore_support, roc_auc_score
from keras.utils import np_utils

from keras import backend as K
K.set_image_dim_ordering('tf')
import itertools
from keras.regularizers import L1L2
from sklearn.preprocessing import normalize
from sklearn.utils import shuffle

import numpy as np
import pandas as pd
np.random.seed(10)

from time import time
import numpy as np
import keras.backend as K
from keras.engine.topology import Layer, InputSpec
from keras.layers import Dense, Input
from keras.models import Model
from keras.optimizers import Adagrad
from keras import callbacks
from keras.initializers import VarianceScaling
from sklearn.cluster import KMeans
from sklearn import metrics
from sklearn.metrics.cluster import normalized_mutual_info_score
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics import accuracy_score
from sklearn import manifold
import keras.layers.normalization as bn
def prepareData(classification_type):
df = pd.read_csv("all_gene_input_data.csv", sep="\t")

from sklearn.utils import shuffle
import os
import keras

from sklearn.preprocessing import normalize
from sklearn.preprocessing import normalize
from sklearn import metrics
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import classification_report
from keras import regularizers

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Convolution2D, UpSampling1D, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Dense
from keras.layers import Flatten, Dense, Reshape
from keras.layers import LSTM
from keras.layers.embeddings import Embedding

from keras.layers import Input, Dense, LSTM, MaxPooling1D, Conv1D, RepeatVector
from keras.models import Model

import pandas

def prepareData(classification_type):
df = pd.read_csv("all_gene_input_data.csv", sep='\t')

df = df.drop(df.columns[[0]], axis=1)
if classification_type =='type':
df = df[df['ctype'] != 15]
for val in range(1, np.unique(df['ctype']).size + 1):
df.ix[df['ctype'] == val, 'ctype'] = val - 1 # labels needs to start from 0
if classification_type =='identification':
df.ix[df['ctype'] != 15, 'ctype'] = 0
df.ix[df['ctype'] == 15, 'ctype'] = 1

if classification_type == "type":
df = df[df["ctype"] != 15]
for val in range(1, np.unique(df["ctype"]).size + 1):
df.ix[df["ctype"] == val, "ctype"] = val - 1 # labels needs to start from 0

if classification_type == "identification":
df.ix[df["ctype"] != 15, "ctype"] = 0
df.ix[df["ctype"] == 15, "ctype"] = 1

column_headers = df.columns.values.tolist()
column_headers.remove('ctype')
column_headers.remove("ctype")

features = df[column_headers].values
labels = df['ctype'].values

normalizedFeatures = normalize(features.reshape(features.shape[0],-1), norm='max', axis=0).reshape(features.shape)

labels = df["ctype"].values

normalizedFeatures = normalize(
features.reshape(features.shape[0], -1), norm="max", axis=0
).reshape(features.shape)

print(type(normalizedFeatures))
print(type(labels))

#features = binary[cols]
#labels = binary['ctype']
# features = binary[cols]
# labels = binary['ctype']

features = np.array(normalizedFeatures)
labels = np.array(labels)

print(features.shape)
print(labels.shape)

#print(images.shape)
features, labels = shuffle(features, labels, random_state = 0) # shuffle the data
# print(images.shape)
features, labels = shuffle(features, labels, random_state=0) # shuffle the data
print("Shauffle completed!")

return features, labels


Expand All @@ -132,7 +84,7 @@ def prepareData(classification_type):
def one_hot_encode(labels):
n_labels = len(labels)
n_unique_labels = len(np.unique(labels))
one_hot_encode = np.zeros((n_labels,n_unique_labels))
one_hot_encode = np.zeros((n_labels, n_unique_labels))
one_hot_encode[np.arange(n_labels), labels] = 1
return one_hot_encode

Expand All @@ -141,35 +93,38 @@ def one_hot_encode(labels):


def prepare_test_train_valid(features, labels):
train_x, test_x, train_y, test_y = train_test_split(features, labels, test_size=0.25, random_state=12345)

print('X_train shape:', train_x.shape)
print('Y_train shape:', train_y.shape)

train_x = np.reshape(train_x,(train_x.shape[0], 1, train_x.shape[1]))
test_x = np.reshape(test_x,(test_x.shape[0], 1, test_x.shape[1]))

train_x, test_x, train_y, test_y = train_test_split(
features, labels, test_size=0.25, random_state=12345
)

print("X_train shape:", train_x.shape)
print("Y_train shape:", train_y.shape)

train_x = np.reshape(train_x, (train_x.shape[0], 1, train_x.shape[1]))
test_x = np.reshape(test_x, (test_x.shape[0], 1, test_x.shape[1]))

return train_x, test_x, train_y, test_y


# In[5]:


type_features, type_labels = prepareData('type')
type_features, type_labels = prepareData("type")


# In[125]:


labels = one_hot_encode(type_labels)

from sklearn.utils import class_weight

train_x, test_x, train_y, test_y = prepare_test_train_valid(type_features, labels)

sample_weights = class_weight.compute_sample_weight('balanced', train_y)
sample_weights = class_weight.compute_sample_weight("balanced", train_y)
print(train_x.shape)
train_x = train_x.transpose(0,2,1)
test_x = test_x.transpose(0,2,1)
train_x = train_x.transpose(0, 2, 1)
test_x = test_x.transpose(0, 2, 1)

print(train_x.shape)

Expand Down Expand Up @@ -205,7 +160,7 @@ def prepare_test_train_valid(features, labels):
inChannel = 1
x, y = 1, 20308
input_layer = Input(shape=(20308, 1))
#input_img = Input(shape = (x, y, inChannel))
# input_img = Input(shape = (x, y, inChannel))
num_classes = 14


Expand All @@ -217,63 +172,69 @@ def prepare_test_train_valid(features, labels):


def encoder(input_layer):
conv1 = Conv1D(32, 2, activation='relu', padding='same')(input_layer) #28 x 28 x 32
conv1 = Conv1D(32, 2, activation="relu", padding="same")(
input_layer
) # 28 x 28 x 32
conv1 = BatchNormalization()(conv1)
conv1 = Conv1D(32, 2, activation='relu', padding='same')(conv1)
conv1 = Conv1D(32, 2, activation="relu", padding="same")(conv1)
conv1 = BatchNormalization()(conv1)
pool1 = MaxPooling1D(pool_size=1, strides=2)(conv1) #14 x 14 x 32
conv2 = Conv1D(64, 2, activation='relu', padding='same')(pool1) #14 x 14 x 64
pool1 = MaxPooling1D(pool_size=1, strides=2)(conv1) # 14 x 14 x 32
conv2 = Conv1D(64, 2, activation="relu", padding="same")(pool1) # 14 x 14 x 64
conv2 = BatchNormalization()(conv2)
conv2 = Conv1D(64, 2, activation='relu', padding='same')(conv2)
conv2 = Conv1D(64, 2, activation="relu", padding="same")(conv2)
conv2 = BatchNormalization()(conv2)
pool2 = MaxPooling1D(pool_size=1, strides=2)(conv2) #7 x 7 x 64
conv3 = Conv1D(128, 2, activation='relu', padding='same')(pool2) #7 x 7 x 128 (small and thick)
pool2 = MaxPooling1D(pool_size=1, strides=2)(conv2) # 7 x 7 x 64
conv3 = Conv1D(128, 2, activation="relu", padding="same")(
pool2
) # 7 x 7 x 128 (small and thick)
conv3 = BatchNormalization()(conv3)
conv3 = Conv1D(128, 2, activation='relu', padding='same')(conv3)
conv3 = Conv1D(128, 2, activation="relu", padding="same")(conv3)
conv3 = BatchNormalization()(conv3)
conv4 = Conv1D(256, 2, activation='relu', padding='same')(conv3) #7 x 7 x 256 (small and thick)
conv4 = Conv1D(256, 2, activation="relu", padding="same")(
conv3
) # 7 x 7 x 256 (small and thick)
conv4 = BatchNormalization()(conv4)
conv4 = Conv1D(256, 2, activation='relu', padding='same')(conv4)
conv4 = Conv1D(256, 2, activation="relu", padding="same")(conv4)
conv4 = BatchNormalization()(conv4)

return conv4


# In[116]:


def decoder(conv4):
conv5 = Conv1D(128, 2, activation='relu', padding='same')(conv4) #7 x 7 x 128
def decoder(conv4):
conv5 = Conv1D(128, 2, activation="relu", padding="same")(conv4) # 7 x 7 x 128
conv5 = BatchNormalization()(conv5)
conv5 = Conv1D(128, 2, activation='relu', padding='same')(conv5)
conv5 = Conv1D(128, 2, activation="relu", padding="same")(conv5)
conv5 = BatchNormalization()(conv5)
conv6 = Conv1D(64, 2, activation='relu', padding='same')(conv5) #7 x 7 x 64
conv6 = Conv1D(64, 2, activation="relu", padding="same")(conv5) # 7 x 7 x 64
conv6 = BatchNormalization()(conv6)
conv6 = Conv1D(64, 2, activation='relu', padding='same')(conv6)
conv6 = Conv1D(64, 2, activation="relu", padding="same")(conv6)
conv6 = BatchNormalization()(conv6)
up1 = UpSampling1D(2)(conv6) #14 x 14 x 64
conv7 = Conv1D(32, 2, activation='relu', padding='same')(up1) # 14 x 14 x 32
up1 = UpSampling1D(2)(conv6) # 14 x 14 x 64
conv7 = Conv1D(32, 2, activation="relu", padding="same")(up1) # 14 x 14 x 32
conv7 = BatchNormalization()(conv7)
conv7 = Conv1D(32, 2, activation='relu', padding='same')(conv7)
conv7 = Conv1D(32, 2, activation="relu", padding="same")(conv7)
conv7 = BatchNormalization()(conv7)
up2 = UpSampling1D(2)(conv7) # 28 x 28 x 32
decoded = Conv1D(1, 2, activation='sigmoid', padding='same')(up2) # 28 x 28 x 1
up2 = UpSampling1D(2)(conv7) # 28 x 28 x 32
decoded = Conv1D(1, 2, activation="sigmoid", padding="same")(up2) # 28 x 28 x 1

return decoded


# In[117]:


autoencoder = Model(input_layer, decoder(encoder(input_layer)))
autoencoder.compile(loss='mean_squared_error', optimizer = RMSprop())
autoencoder.compile(loss="mean_squared_error", optimizer=RMSprop())

autoencoder.summary()


# In[ ]:


autoencoder_train = autoencoder.fit(train_x, train_x, batch_size=32,epochs=1,verbose=1,validation_split=0.1)


autoencoder_train = autoencoder.fit(
train_x, train_x, batch_size=32, epochs=1, verbose=1, validation_split=0.1
)
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