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stringlengths 13
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128049433/cell_25 | [
"image_output_1.png"
] | from tensorflow.keras import models,layers
import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
def get_dataset(ds, train_split=0.8, val_split=0.1, test_split=0.1, shuffle=True, shuffle_size=10000):
ds_size = len(ds)
if shuffle:
ds = ds.shuffle(shuffle_size, seed=8)
train_size = int(train_split * ds_size)
val_size = int(val_split * ds_size)
train_ds = ds.take(train_size)
val_ds = ds.skip(train_size).take(val_size)
test_ds = ds.skip(train_size).skip(val_size)
return (train_ds, val_ds, test_ds)
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
val_ds = val_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
test_ds = test_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
resize_and_rescale = tf.keras.Sequential([layers.experimental.preprocessing.Resizing(256, 256), layers.experimental.preprocessing.Rescaling(1.0 / 255)])
data_augmentation = tf.keras.Sequential([layers.experimental.preprocessing.RandomFlip('horizontal_and_vertical'), layers.experimental.preprocessing.RandomRotation(0.3)])
n_classes = 4
input_shape = (BATCH_SIZE, 256, 256, 3)
model = models.Sequential([resize_and_rescale, data_augmentation, layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Conv2D(128, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Flatten(), layers.Dense(256, activation='relu'), layers.Dense(128, activation='relu'), layers.Dense(64, activation='relu'), layers.Dense(n_classes, activation='softmax')])
model.build(input_shape=input_shape)
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['accuracy'])
history = model.fit(train_ds, epochs=60, batch_size=BATCH_SIZE, verbose=1, validation_data=val_ds) | code |
128049433/cell_30 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import glob as gb
import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import numpy as np
import pandas as pd
import os
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
import os
import glob as gb
path = '//kaggle//input//corn-or-maize-leaf-disease-dataset//data'
size = []
for folder in os.listdir(path):
files = gb.glob(pathname=str(path + '//' + folder + '/*.jpg'))
for file in files:
image = plt.imread(file)
size.append(image.shape)
pd.Series(size).value_counts()
class_names = dataset.class_names
class_names
plt.figure(figsize=(10,10))
for image_batch , label_batch in dataset.take(1):
for i in range(12):
ax = plt.subplot(3,4,i+1)
plt.imshow(image_batch[i].numpy().astype('uint8'))
plt.title(class_names[label_batch[i]])
plt.axis('off')
def get_dataset(ds, train_split=0.8, val_split=0.1, test_split=0.1, shuffle=True, shuffle_size=10000):
ds_size = len(ds)
if shuffle:
ds = ds.shuffle(shuffle_size, seed=8)
train_size = int(train_split * ds_size)
val_size = int(val_split * ds_size)
train_ds = ds.take(train_size)
val_ds = ds.skip(train_size).take(val_size)
test_ds = ds.skip(train_size).skip(val_size)
return (train_ds, val_ds, test_ds)
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
val_ds = val_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
test_ds = test_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
resize_and_rescale = tf.keras.Sequential([layers.experimental.preprocessing.Resizing(256, 256), layers.experimental.preprocessing.Rescaling(1.0 / 255)])
data_augmentation = tf.keras.Sequential([layers.experimental.preprocessing.RandomFlip('horizontal_and_vertical'), layers.experimental.preprocessing.RandomRotation(0.3)])
n_classes = 4
input_shape = (BATCH_SIZE, 256, 256, 3)
model = models.Sequential([resize_and_rescale, data_augmentation, layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Conv2D(128, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Flatten(), layers.Dense(256, activation='relu'), layers.Dense(128, activation='relu'), layers.Dense(64, activation='relu'), layers.Dense(n_classes, activation='softmax')])
model.build(input_shape=input_shape)
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['accuracy'])
history = model.fit(train_ds, epochs=60, batch_size=BATCH_SIZE, verbose=1, validation_data=val_ds)
history.params
history.history.keys()
his_data = pd.DataFrame(history.history)
plt.figure(figsize=(20, 5))
plt.subplot(1, 2, 1)
plt.plot(his_data.loss, label='Training loss')
plt.plot(his_data.val_loss, label='Validation loss')
plt.xlabel('Epochs')
plt.ylabel('loss')
plt.title('Losses')
plt.grid()
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(his_data.accuracy, label='Training accuracy')
plt.plot(his_data.val_accuracy, label='Validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.title('Accuracy')
plt.grid()
plt.legend() | code |
128049433/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE) | code |
128049433/cell_29 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
def get_dataset(ds, train_split=0.8, val_split=0.1, test_split=0.1, shuffle=True, shuffle_size=10000):
ds_size = len(ds)
if shuffle:
ds = ds.shuffle(shuffle_size, seed=8)
train_size = int(train_split * ds_size)
val_size = int(val_split * ds_size)
train_ds = ds.take(train_size)
val_ds = ds.skip(train_size).take(val_size)
test_ds = ds.skip(train_size).skip(val_size)
return (train_ds, val_ds, test_ds)
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
val_ds = val_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
test_ds = test_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
resize_and_rescale = tf.keras.Sequential([layers.experimental.preprocessing.Resizing(256, 256), layers.experimental.preprocessing.Rescaling(1.0 / 255)])
data_augmentation = tf.keras.Sequential([layers.experimental.preprocessing.RandomFlip('horizontal_and_vertical'), layers.experimental.preprocessing.RandomRotation(0.3)])
n_classes = 4
input_shape = (BATCH_SIZE, 256, 256, 3)
model = models.Sequential([resize_and_rescale, data_augmentation, layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Conv2D(128, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Flatten(), layers.Dense(256, activation='relu'), layers.Dense(128, activation='relu'), layers.Dense(64, activation='relu'), layers.Dense(n_classes, activation='softmax')])
model.build(input_shape=input_shape)
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['accuracy'])
history = model.fit(train_ds, epochs=60, batch_size=BATCH_SIZE, verbose=1, validation_data=val_ds)
history.params
history.history.keys() | code |
128049433/cell_26 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
def get_dataset(ds, train_split=0.8, val_split=0.1, test_split=0.1, shuffle=True, shuffle_size=10000):
ds_size = len(ds)
if shuffle:
ds = ds.shuffle(shuffle_size, seed=8)
train_size = int(train_split * ds_size)
val_size = int(val_split * ds_size)
train_ds = ds.take(train_size)
val_ds = ds.skip(train_size).take(val_size)
test_ds = ds.skip(train_size).skip(val_size)
return (train_ds, val_ds, test_ds)
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
val_ds = val_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
test_ds = test_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
resize_and_rescale = tf.keras.Sequential([layers.experimental.preprocessing.Resizing(256, 256), layers.experimental.preprocessing.Rescaling(1.0 / 255)])
data_augmentation = tf.keras.Sequential([layers.experimental.preprocessing.RandomFlip('horizontal_and_vertical'), layers.experimental.preprocessing.RandomRotation(0.3)])
n_classes = 4
input_shape = (BATCH_SIZE, 256, 256, 3)
model = models.Sequential([resize_and_rescale, data_augmentation, layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Conv2D(128, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Flatten(), layers.Dense(256, activation='relu'), layers.Dense(128, activation='relu'), layers.Dense(64, activation='relu'), layers.Dense(n_classes, activation='softmax')])
model.build(input_shape=input_shape)
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['accuracy'])
history = model.fit(train_ds, epochs=60, batch_size=BATCH_SIZE, verbose=1, validation_data=val_ds)
scores = model.evaluate(test_ds) | code |
128049433/cell_11 | [
"text_plain_output_1.png"
] | import glob as gb
import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import numpy as np
import pandas as pd
import os
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
import os
import glob as gb
path = '//kaggle//input//corn-or-maize-leaf-disease-dataset//data'
size = []
for folder in os.listdir(path):
files = gb.glob(pathname=str(path + '//' + folder + '/*.jpg'))
for file in files:
image = plt.imread(file)
size.append(image.shape)
pd.Series(size).value_counts()
class_names = dataset.class_names
class_names
plt.figure(figsize=(10, 10))
for image_batch, label_batch in dataset.take(1):
for i in range(12):
ax = plt.subplot(3, 4, i + 1)
plt.imshow(image_batch[i].numpy().astype('uint8'))
plt.title(class_names[label_batch[i]])
plt.axis('off') | code |
128049433/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128049433/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import glob as gb
import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import glob as gb
path = '//kaggle//input//corn-or-maize-leaf-disease-dataset//data'
size = []
for folder in os.listdir(path):
files = gb.glob(pathname=str(path + '//' + folder + '/*.jpg'))
for file in files:
image = plt.imread(file)
size.append(image.shape)
pd.Series(size).value_counts() | code |
128049433/cell_32 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import glob as gb
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import numpy as np
import pandas as pd
import os
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
import os
import glob as gb
path = '//kaggle//input//corn-or-maize-leaf-disease-dataset//data'
size = []
for folder in os.listdir(path):
files = gb.glob(pathname=str(path + '//' + folder + '/*.jpg'))
for file in files:
image = plt.imread(file)
size.append(image.shape)
pd.Series(size).value_counts()
class_names = dataset.class_names
class_names
plt.figure(figsize=(10,10))
for image_batch , label_batch in dataset.take(1):
for i in range(12):
ax = plt.subplot(3,4,i+1)
plt.imshow(image_batch[i].numpy().astype('uint8'))
plt.title(class_names[label_batch[i]])
plt.axis('off')
def get_dataset(ds, train_split=0.8, val_split=0.1, test_split=0.1, shuffle=True, shuffle_size=10000):
ds_size = len(ds)
if shuffle:
ds = ds.shuffle(shuffle_size, seed=8)
train_size = int(train_split * ds_size)
val_size = int(val_split * ds_size)
train_ds = ds.take(train_size)
val_ds = ds.skip(train_size).take(val_size)
test_ds = ds.skip(train_size).skip(val_size)
return (train_ds, val_ds, test_ds)
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
val_ds = val_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
test_ds = test_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
resize_and_rescale = tf.keras.Sequential([layers.experimental.preprocessing.Resizing(256, 256), layers.experimental.preprocessing.Rescaling(1.0 / 255)])
data_augmentation = tf.keras.Sequential([layers.experimental.preprocessing.RandomFlip('horizontal_and_vertical'), layers.experimental.preprocessing.RandomRotation(0.3)])
n_classes = 4
input_shape = (BATCH_SIZE, 256, 256, 3)
model = models.Sequential([resize_and_rescale, data_augmentation, layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Conv2D(128, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Flatten(), layers.Dense(256, activation='relu'), layers.Dense(128, activation='relu'), layers.Dense(64, activation='relu'), layers.Dense(n_classes, activation='softmax')])
model.build(input_shape=input_shape)
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['accuracy'])
history = model.fit(train_ds, epochs=60, batch_size=BATCH_SIZE, verbose=1, validation_data=val_ds)
scores = model.evaluate(test_ds)
history.params
history.history.keys()
his_data = pd.DataFrame(history.history)
import numpy as np
for images_batch, labels_batch in test_ds.take(1):
first_image = images_batch[0].numpy().astype('uint8')
first_label = labels_batch[0].numpy()
print('First Image to Predict :')
plt.imshow(first_image)
print('\nActual label:', class_names[first_label])
batch_prediction = model.predict(images_batch)
print('\nPredicted label', class_names[np.argmax(batch_prediction[0])]) | code |
128049433/cell_28 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
def get_dataset(ds, train_split=0.8, val_split=0.1, test_split=0.1, shuffle=True, shuffle_size=10000):
ds_size = len(ds)
if shuffle:
ds = ds.shuffle(shuffle_size, seed=8)
train_size = int(train_split * ds_size)
val_size = int(val_split * ds_size)
train_ds = ds.take(train_size)
val_ds = ds.skip(train_size).take(val_size)
test_ds = ds.skip(train_size).skip(val_size)
return (train_ds, val_ds, test_ds)
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
val_ds = val_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
test_ds = test_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
resize_and_rescale = tf.keras.Sequential([layers.experimental.preprocessing.Resizing(256, 256), layers.experimental.preprocessing.Rescaling(1.0 / 255)])
data_augmentation = tf.keras.Sequential([layers.experimental.preprocessing.RandomFlip('horizontal_and_vertical'), layers.experimental.preprocessing.RandomRotation(0.3)])
n_classes = 4
input_shape = (BATCH_SIZE, 256, 256, 3)
model = models.Sequential([resize_and_rescale, data_augmentation, layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Conv2D(128, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Flatten(), layers.Dense(256, activation='relu'), layers.Dense(128, activation='relu'), layers.Dense(64, activation='relu'), layers.Dense(n_classes, activation='softmax')])
model.build(input_shape=input_shape)
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['accuracy'])
history = model.fit(train_ds, epochs=60, batch_size=BATCH_SIZE, verbose=1, validation_data=val_ds)
history.params | code |
128049433/cell_15 | [
"text_plain_output_1.png"
] | def get_dataset(ds, train_split=0.8, val_split=0.1, test_split=0.1, shuffle=True, shuffle_size=10000):
ds_size = len(ds)
if shuffle:
ds = ds.shuffle(shuffle_size, seed=8)
train_size = int(train_split * ds_size)
val_size = int(val_split * ds_size)
train_ds = ds.take(train_size)
val_ds = ds.skip(train_size).take(val_size)
test_ds = ds.skip(train_size).skip(val_size)
return (train_ds, val_ds, test_ds)
print('Length of Training Dataset is', len(train_ds))
print('\nLength of Validation Dataset is', len(val_ds))
print('\nLength of Testing Dataset is', len(test_ds)) | code |
128049433/cell_10 | [
"text_plain_output_1.png"
] | import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
class_names = dataset.class_names
class_names
len(dataset) | code |
128049433/cell_27 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models,layers
import tensorflow as tf
IMAGE_SIZE = (256, 256)
BATCH_SIZE = 32
CHANNELS = 3
EPOCHES = 100
dataset = tf.keras.preprocessing.image_dataset_from_directory('/kaggle/input/corn-or-maize-leaf-disease-dataset/data', shuffle=True, image_size=IMAGE_SIZE, batch_size=BATCH_SIZE)
def get_dataset(ds, train_split=0.8, val_split=0.1, test_split=0.1, shuffle=True, shuffle_size=10000):
ds_size = len(ds)
if shuffle:
ds = ds.shuffle(shuffle_size, seed=8)
train_size = int(train_split * ds_size)
val_size = int(val_split * ds_size)
train_ds = ds.take(train_size)
val_ds = ds.skip(train_size).take(val_size)
test_ds = ds.skip(train_size).skip(val_size)
return (train_ds, val_ds, test_ds)
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
val_ds = val_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
test_ds = test_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)
resize_and_rescale = tf.keras.Sequential([layers.experimental.preprocessing.Resizing(256, 256), layers.experimental.preprocessing.Rescaling(1.0 / 255)])
data_augmentation = tf.keras.Sequential([layers.experimental.preprocessing.RandomFlip('horizontal_and_vertical'), layers.experimental.preprocessing.RandomRotation(0.3)])
n_classes = 4
input_shape = (BATCH_SIZE, 256, 256, 3)
model = models.Sequential([resize_and_rescale, data_augmentation, layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Conv2D(128, kernel_size=(3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Flatten(), layers.Dense(256, activation='relu'), layers.Dense(128, activation='relu'), layers.Dense(64, activation='relu'), layers.Dense(n_classes, activation='softmax')])
model.build(input_shape=input_shape)
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['accuracy'])
history = model.fit(train_ds, epochs=60, batch_size=BATCH_SIZE, verbose=1, validation_data=val_ds)
history | code |
128028409/cell_13 | [
"text_plain_output_1.png"
] | import pygad
import random
target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].split(')')[0].split(', '))
n = len(outputs)
edges = []
for i in range(n):
out = outputs[i]
for j in range(n):
if out in inputs[j]:
edges.append([i + 1, j + 1])
nodesLen = n
edgesLen = len(edges)
def IC(chromosome):
zeros = 0
ones = 0
for i in chromosome:
if i == '0':
zeros += 1
else:
ones += 1
return abs(zeros - ones)
IC('0000000111111111111')
def PC(chromosome, edges):
partition0 = []
partition1 = []
for i in range(len(chromosome)):
if chromosome[i] == '0':
partition0.append(i + 1)
else:
partition1.append(i + 1)
pc = 0
for i in edges:
x, y = (i[0], i[1])
if x in partition0 and y in partition1 or (x in partition1 and y in partition0):
pc += 1
return pc
a = 100
b = 10
edgesTemp = [[1, 2], [1, 3], [2, 4], [3, 6], [4, 5], [5, 6]]
def fitness(ga_instance, chromosome, chromosomeIdx):
ic = IC(chromosome)
pc = PC(chromosome, edges)
cost = a * pc + b * ic * ic
return 1.0 / cost
def on_generation(ga_instance):
pass
import random
def getInitialPopulation(chromosomeLen, chromosomesReq):
population = []
for i in range(chromosomesReq):
chromosome = []
for j in range(chromosomeLen):
chromosome.append(random.randint(0, 1))
population.append(chromosome)
return population
num_generations = 50
num_parents_mating = 2
init_range_low = 0
init_range_high = 2
initial_pop = getInitialPopulation(nodesLen, 20)
ga_instance = pygad.GA(num_generations=num_generations, num_parents_mating=num_parents_mating, fitness_func=fitness, sol_per_pop=10, num_genes=nodesLen, gene_type=int, init_range_low=init_range_low, init_range_high=init_range_high, gene_space=[0, 1], initial_population=initial_pop, parent_selection_type='sss', K_tournament=3, crossover_type='scattered', crossover_probability=0.6, mutation_type='random', mutation_probability=0.1, save_best_solutions=True, on_generation=on_generation)
ga_instance.run() | code |
128028409/cell_4 | [
"text_plain_output_1.png"
] | target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].split(')')[0].split(', '))
n = len(outputs)
edges = []
for i in range(n):
out = outputs[i]
for j in range(n):
if out in inputs[j]:
edges.append([i + 1, j + 1])
nodesLen = n
edgesLen = len(edges)
print(nodesLen, edgesLen)
print(edges) | code |
128028409/cell_6 | [
"text_plain_output_1.png"
] | target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].split(')')[0].split(', '))
n = len(outputs)
edges = []
for i in range(n):
out = outputs[i]
for j in range(n):
if out in inputs[j]:
edges.append([i + 1, j + 1])
def PC(chromosome, edges):
partition0 = []
partition1 = []
for i in range(len(chromosome)):
if chromosome[i] == '0':
partition0.append(i + 1)
else:
partition1.append(i + 1)
pc = 0
for i in edges:
x, y = (i[0], i[1])
if x in partition0 and y in partition1 or (x in partition1 and y in partition0):
pc += 1
return pc
print(PC('000111', [[1, 2], [1, 3], [2, 4], [3, 6], [4, 5], [5, 6]])) | code |
128028409/cell_2 | [
"text_plain_output_1.png"
] | target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].split(')')[0].split(', '))
print('node\toutputs', '\t', 'inputs\n')
for i in range(len(outputs)):
print(i + 1, '\t', outputs[i], '\t\t', inputs[i]) | code |
128028409/cell_8 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install pygad
import pygad | code |
128028409/cell_15 | [
"text_plain_output_1.png"
] | import pygad
import random
target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].split(')')[0].split(', '))
n = len(outputs)
edges = []
for i in range(n):
out = outputs[i]
for j in range(n):
if out in inputs[j]:
edges.append([i + 1, j + 1])
nodesLen = n
edgesLen = len(edges)
def IC(chromosome):
zeros = 0
ones = 0
for i in chromosome:
if i == '0':
zeros += 1
else:
ones += 1
return abs(zeros - ones)
IC('0000000111111111111')
def PC(chromosome, edges):
partition0 = []
partition1 = []
for i in range(len(chromosome)):
if chromosome[i] == '0':
partition0.append(i + 1)
else:
partition1.append(i + 1)
pc = 0
for i in edges:
x, y = (i[0], i[1])
if x in partition0 and y in partition1 or (x in partition1 and y in partition0):
pc += 1
return pc
a = 100
b = 10
edgesTemp = [[1, 2], [1, 3], [2, 4], [3, 6], [4, 5], [5, 6]]
def fitness(ga_instance, chromosome, chromosomeIdx):
ic = IC(chromosome)
pc = PC(chromosome, edges)
cost = a * pc + b * ic * ic
return 1.0 / cost
def on_generation(ga_instance):
pass
import random
def getInitialPopulation(chromosomeLen, chromosomesReq):
population = []
for i in range(chromosomesReq):
chromosome = []
for j in range(chromosomeLen):
chromosome.append(random.randint(0, 1))
population.append(chromosome)
return population
num_generations = 50
num_parents_mating = 2
init_range_low = 0
init_range_high = 2
initial_pop = getInitialPopulation(nodesLen, 20)
ga_instance = pygad.GA(num_generations=num_generations, num_parents_mating=num_parents_mating, fitness_func=fitness, sol_per_pop=10, num_genes=nodesLen, gene_type=int, init_range_low=init_range_low, init_range_high=init_range_high, gene_space=[0, 1], initial_population=initial_pop, parent_selection_type='sss', K_tournament=3, crossover_type='scattered', crossover_probability=0.6, mutation_type='random', mutation_probability=0.1, save_best_solutions=True, on_generation=on_generation)
ga_instance.run()
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print('Parameters of the best solution : {solution}'.format(solution=solution))
print('Fitness value of the best solution = {solution_fitness}'.format(solution_fitness=solution_fitness))
print(solution_idx) | code |
128028409/cell_16 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].split(')')[0].split(', '))
n = len(outputs)
edges = []
for i in range(n):
out = outputs[i]
for j in range(n):
if out in inputs[j]:
edges.append([i + 1, j + 1])
nodesLen = n
edgesLen = len(edges)
def IC(chromosome):
zeros = 0
ones = 0
for i in chromosome:
if i == '0':
zeros += 1
else:
ones += 1
return abs(zeros - ones)
IC('0000000111111111111')
def PC(chromosome, edges):
partition0 = []
partition1 = []
for i in range(len(chromosome)):
if chromosome[i] == '0':
partition0.append(i + 1)
else:
partition1.append(i + 1)
pc = 0
for i in edges:
x, y = (i[0], i[1])
if x in partition0 and y in partition1 or (x in partition1 and y in partition0):
pc += 1
return pc
a = 100
b = 10
edgesTemp = [[1, 2], [1, 3], [2, 4], [3, 6], [4, 5], [5, 6]]
def fitness(ga_instance, chromosome, chromosomeIdx):
ic = IC(chromosome)
pc = PC(chromosome, edges)
cost = a * pc + b * ic * ic
return 1.0 / cost
from tqdm import tqdm
maxFitness = 0
res = ''
for i in tqdm(range(1 << nodesLen)):
s = ''
for j in range(nodesLen):
if i & 1 << j == 0:
s += '0'
else:
s += '1'
val = fitness(0, s, 0)
if val > maxFitness:
maxFitness = val
res = s
print(f'{res}<------------>{maxFitness}') | code |
128028409/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].split(')')[0].split(', '))
print('edges\n')
n = len(outputs)
edges = []
for i in range(n):
out = outputs[i]
for j in range(n):
if out in inputs[j]:
edges.append([i + 1, j + 1])
print(i + 1, '\t', j + 1) | code |
128028409/cell_14 | [
"text_plain_output_1.png"
] | import pygad
import random
target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].split(')')[0].split(', '))
n = len(outputs)
edges = []
for i in range(n):
out = outputs[i]
for j in range(n):
if out in inputs[j]:
edges.append([i + 1, j + 1])
nodesLen = n
edgesLen = len(edges)
def IC(chromosome):
zeros = 0
ones = 0
for i in chromosome:
if i == '0':
zeros += 1
else:
ones += 1
return abs(zeros - ones)
IC('0000000111111111111')
def PC(chromosome, edges):
partition0 = []
partition1 = []
for i in range(len(chromosome)):
if chromosome[i] == '0':
partition0.append(i + 1)
else:
partition1.append(i + 1)
pc = 0
for i in edges:
x, y = (i[0], i[1])
if x in partition0 and y in partition1 or (x in partition1 and y in partition0):
pc += 1
return pc
a = 100
b = 10
edgesTemp = [[1, 2], [1, 3], [2, 4], [3, 6], [4, 5], [5, 6]]
def fitness(ga_instance, chromosome, chromosomeIdx):
ic = IC(chromosome)
pc = PC(chromosome, edges)
cost = a * pc + b * ic * ic
return 1.0 / cost
def on_generation(ga_instance):
pass
import random
def getInitialPopulation(chromosomeLen, chromosomesReq):
population = []
for i in range(chromosomesReq):
chromosome = []
for j in range(chromosomeLen):
chromosome.append(random.randint(0, 1))
population.append(chromosome)
return population
num_generations = 50
num_parents_mating = 2
init_range_low = 0
init_range_high = 2
initial_pop = getInitialPopulation(nodesLen, 20)
ga_instance = pygad.GA(num_generations=num_generations, num_parents_mating=num_parents_mating, fitness_func=fitness, sol_per_pop=10, num_genes=nodesLen, gene_type=int, init_range_low=init_range_low, init_range_high=init_range_high, gene_space=[0, 1], initial_population=initial_pop, parent_selection_type='sss', K_tournament=3, crossover_type='scattered', crossover_probability=0.6, mutation_type='random', mutation_probability=0.1, save_best_solutions=True, on_generation=on_generation)
ga_instance.run()
ga_instance.plot_fitness() | code |
128028409/cell_12 | [
"text_plain_output_1.png"
] | import pygad
import random
target_url = 'https://filebox.ece.vt.edu/~mhsiao/ISCAS89/s27.bench'
import urllib.request
outputs = []
inputs = []
for line in urllib.request.urlopen(target_url):
l = line.decode('utf-8')
if '=' in l:
parse = l.split(' = ')
outputs.append(parse[0])
inputs.append(parse[1].split('(')[1].split(')')[0].split(', '))
n = len(outputs)
edges = []
for i in range(n):
out = outputs[i]
for j in range(n):
if out in inputs[j]:
edges.append([i + 1, j + 1])
nodesLen = n
edgesLen = len(edges)
def IC(chromosome):
zeros = 0
ones = 0
for i in chromosome:
if i == '0':
zeros += 1
else:
ones += 1
return abs(zeros - ones)
IC('0000000111111111111')
def PC(chromosome, edges):
partition0 = []
partition1 = []
for i in range(len(chromosome)):
if chromosome[i] == '0':
partition0.append(i + 1)
else:
partition1.append(i + 1)
pc = 0
for i in edges:
x, y = (i[0], i[1])
if x in partition0 and y in partition1 or (x in partition1 and y in partition0):
pc += 1
return pc
a = 100
b = 10
edgesTemp = [[1, 2], [1, 3], [2, 4], [3, 6], [4, 5], [5, 6]]
def fitness(ga_instance, chromosome, chromosomeIdx):
ic = IC(chromosome)
pc = PC(chromosome, edges)
cost = a * pc + b * ic * ic
return 1.0 / cost
def on_generation(ga_instance):
pass
import random
def getInitialPopulation(chromosomeLen, chromosomesReq):
population = []
for i in range(chromosomesReq):
chromosome = []
for j in range(chromosomeLen):
chromosome.append(random.randint(0, 1))
population.append(chromosome)
return population
num_generations = 50
num_parents_mating = 2
init_range_low = 0
init_range_high = 2
initial_pop = getInitialPopulation(nodesLen, 20)
ga_instance = pygad.GA(num_generations=num_generations, num_parents_mating=num_parents_mating, fitness_func=fitness, sol_per_pop=10, num_genes=nodesLen, gene_type=int, init_range_low=init_range_low, init_range_high=init_range_high, gene_space=[0, 1], initial_population=initial_pop, parent_selection_type='sss', K_tournament=3, crossover_type='scattered', crossover_probability=0.6, mutation_type='random', mutation_probability=0.1, save_best_solutions=True, on_generation=on_generation) | code |
128028409/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | def IC(chromosome):
zeros = 0
ones = 0
for i in chromosome:
if i == '0':
zeros += 1
else:
ones += 1
return abs(zeros - ones)
IC('0000000111111111111') | code |
17102411/cell_13 | [
"text_plain_output_1.png"
] | data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data['rest_type'].value_counts() | code |
17102411/cell_9 | [
"text_plain_output_1.png"
] | data.shape
data.isnull().sum()
data.address[1] | code |
17102411/cell_4 | [
"text_html_output_1.png"
] | data.shape
data.info() | code |
17102411/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata.shape
newdata.index = newdata['name']
pd.DataFrame(newdata.groupby(['cuisines'])['cuisines'].agg(['count']).sort_values('count', ascending=False)).head(10) | code |
17102411/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata.shape
newdata.describe(include='all') | code |
17102411/cell_6 | [
"text_html_output_1.png"
] | data.shape
data.tail(3) | code |
17102411/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata.shape
newdata.index = newdata['name']
newdata.drop(['name', 'url', 'phone', 'listed_in(city)', 'listed_in(type)_x', 'address', 'dish_liked', 'listed_in(type)_y', 'menu_item', 'cuisines', 'reviews_list'], axis=1, inplace=True)
newdata.head(3) | code |
17102411/cell_7 | [
"text_html_output_1.png"
] | data.shape
data['menu_item'].value_counts() | code |
17102411/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata.shape | code |
17102411/cell_8 | [
"text_html_output_1.png"
] | data.shape
data.isnull().sum() | code |
17102411/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data[['votes', 'cost']] = data[['votes', 'cost']].apply(pd.to_numeric)
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata.head(3) | code |
17102411/cell_3 | [
"text_plain_output_1.png"
] | data.shape | code |
17102411/cell_12 | [
"text_plain_output_1.png"
] | data.shape
data.isnull().sum()
data.address[1]
data = data.rename({'approx_cost(for two people)': 'cost'}, axis=1)
data['cost'] = data['cost'].replace(',', '', regex=True)
data['listed_in(type)'].value_counts() | code |
17102411/cell_5 | [
"text_plain_output_1.png"
] | data.shape
data.head(3) | code |
16133275/cell_9 | [
"text_plain_output_1.png"
] | from IPython.display import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import tensorflow as tf
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
onlyfiles = os.listdir('../input/utkface_aligned_cropped/UTKFace')
y = np.array([[[i.split('_')[0]], [i.split('_')[1]]] for i in onlyfiles])
X_data = []
for file in onlyfiles:
face = cv2.imread('../input/utkface_aligned_cropped/UTKFace/' + file, cv2.IMREAD_COLOR)
face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
face = cv2.resize(face, (32, 32))
X_data.append(face)
X_data = np.array(X_data)
X_data.shape
X = np.squeeze(X_data)
(plt.xticks([]), plt.yticks([]))
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.33)
y_train = [y_train[:, 1], y_train[:, 0]]
y_valid = [y_valid[:, 1], y_valid[:, 0]]
def gen_model():
inputs = tf.keras.layers.Input(shape=(32, 32, 3))
x = inputs
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.MaxPool2D(2)(x)
x = layers.Dropout(0.3)(x)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.MaxPool2D(2)(x)
x = layers.Dropout(0.3)(x)
x = layers.Conv2D(84, 3, activation='relu')(x)
x = layers.Dropout(0.3)(x)
x = layers.Flatten()(x)
x1 = layers.Dense(64, activation='relu')(x)
x2 = layers.Dense(64, activation='relu')(x)
x1 = layers.Dense(1, activation='sigmoid', name='sex_out')(x1)
x2 = layers.Dense(1, activation='relu', name='age_out')(x2)
model = tf.keras.models.Model(inputs=inputs, outputs=[x1, x2])
model.compile(optimizer='Adam', loss=['binary_crossentropy', 'mae'])
return model
model = gen_model()
Image('model.png')
import random
random_id = random.random()
model.summary()
callbacks = [tf.keras.callbacks.EarlyStopping(patience=75, monitor='val_loss', restore_best_weights=True), tf.keras.callbacks.TensorBoard(log_dir='./logs/' + str(random_id))]
model.fit(X_train, y_train, epochs=2000, batch_size=240, validation_data=(X_valid, y_valid), callbacks=callbacks, shuffle=True)
model.evaluate(X_valid, y_valid) | code |
16133275/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import numpy as np
import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
onlyfiles = os.listdir('../input/utkface_aligned_cropped/UTKFace')
y = np.array([[[i.split('_')[0]], [i.split('_')[1]]] for i in onlyfiles])
X_data = []
for file in onlyfiles:
face = cv2.imread('../input/utkface_aligned_cropped/UTKFace/' + file, cv2.IMREAD_COLOR)
face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
face = cv2.resize(face, (32, 32))
X_data.append(face)
X_data = np.array(X_data)
X_data.shape | code |
16133275/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
print(os.listdir('../input/utkface_aligned_cropped/')) | code |
16133275/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import Image
from tensorflow.keras import layers
import tensorflow as tf
def gen_model():
inputs = tf.keras.layers.Input(shape=(32, 32, 3))
x = inputs
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.MaxPool2D(2)(x)
x = layers.Dropout(0.3)(x)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.MaxPool2D(2)(x)
x = layers.Dropout(0.3)(x)
x = layers.Conv2D(84, 3, activation='relu')(x)
x = layers.Dropout(0.3)(x)
x = layers.Flatten()(x)
x1 = layers.Dense(64, activation='relu')(x)
x2 = layers.Dense(64, activation='relu')(x)
x1 = layers.Dense(1, activation='sigmoid', name='sex_out')(x1)
x2 = layers.Dense(1, activation='relu', name='age_out')(x2)
model = tf.keras.models.Model(inputs=inputs, outputs=[x1, x2])
model.compile(optimizer='Adam', loss=['binary_crossentropy', 'mae'])
tf.keras.utils.plot_model(model, 'model.png', show_shapes=True)
return model
model = gen_model()
Image('model.png') | code |
16133275/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import tensorflow as tf
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
onlyfiles = os.listdir('../input/utkface_aligned_cropped/UTKFace')
y = np.array([[[i.split('_')[0]], [i.split('_')[1]]] for i in onlyfiles])
X_data = []
for file in onlyfiles:
face = cv2.imread('../input/utkface_aligned_cropped/UTKFace/' + file, cv2.IMREAD_COLOR)
face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
face = cv2.resize(face, (32, 32))
X_data.append(face)
X_data = np.array(X_data)
X_data.shape
X = np.squeeze(X_data)
(plt.xticks([]), plt.yticks([]))
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.33)
y_train = [y_train[:, 1], y_train[:, 0]]
y_valid = [y_valid[:, 1], y_valid[:, 0]]
def gen_model():
inputs = tf.keras.layers.Input(shape=(32, 32, 3))
x = inputs
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.MaxPool2D(2)(x)
x = layers.Dropout(0.3)(x)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.MaxPool2D(2)(x)
x = layers.Dropout(0.3)(x)
x = layers.Conv2D(84, 3, activation='relu')(x)
x = layers.Dropout(0.3)(x)
x = layers.Flatten()(x)
x1 = layers.Dense(64, activation='relu')(x)
x2 = layers.Dense(64, activation='relu')(x)
x1 = layers.Dense(1, activation='sigmoid', name='sex_out')(x1)
x2 = layers.Dense(1, activation='relu', name='age_out')(x2)
model = tf.keras.models.Model(inputs=inputs, outputs=[x1, x2])
model.compile(optimizer='Adam', loss=['binary_crossentropy', 'mae'])
return model
model = gen_model()
Image('model.png')
import random
random_id = random.random()
model.summary()
callbacks = [tf.keras.callbacks.EarlyStopping(patience=75, monitor='val_loss', restore_best_weights=True), tf.keras.callbacks.TensorBoard(log_dir='./logs/' + str(random_id))]
model.fit(X_train, y_train, epochs=2000, batch_size=240, validation_data=(X_valid, y_valid), callbacks=callbacks, shuffle=True) | code |
16133275/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
onlyfiles = os.listdir('../input/utkface_aligned_cropped/UTKFace')
y = np.array([[[i.split('_')[0]], [i.split('_')[1]]] for i in onlyfiles])
print(y.shape)
print(y[0]) | code |
16133275/cell_10 | [
"text_plain_output_1.png"
] | from IPython.display import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import tensorflow as tf
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
onlyfiles = os.listdir('../input/utkface_aligned_cropped/UTKFace')
y = np.array([[[i.split('_')[0]], [i.split('_')[1]]] for i in onlyfiles])
X_data = []
for file in onlyfiles:
face = cv2.imread('../input/utkface_aligned_cropped/UTKFace/' + file, cv2.IMREAD_COLOR)
face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
face = cv2.resize(face, (32, 32))
X_data.append(face)
X_data = np.array(X_data)
X_data.shape
X = np.squeeze(X_data)
(plt.xticks([]), plt.yticks([]))
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.33)
y_train = [y_train[:, 1], y_train[:, 0]]
y_valid = [y_valid[:, 1], y_valid[:, 0]]
def gen_model():
inputs = tf.keras.layers.Input(shape=(32, 32, 3))
x = inputs
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.MaxPool2D(2)(x)
x = layers.Dropout(0.3)(x)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.MaxPool2D(2)(x)
x = layers.Dropout(0.3)(x)
x = layers.Conv2D(84, 3, activation='relu')(x)
x = layers.Dropout(0.3)(x)
x = layers.Flatten()(x)
x1 = layers.Dense(64, activation='relu')(x)
x2 = layers.Dense(64, activation='relu')(x)
x1 = layers.Dense(1, activation='sigmoid', name='sex_out')(x1)
x2 = layers.Dense(1, activation='relu', name='age_out')(x2)
model = tf.keras.models.Model(inputs=inputs, outputs=[x1, x2])
model.compile(optimizer='Adam', loss=['binary_crossentropy', 'mae'])
return model
model = gen_model()
Image('model.png')
import random
random_id = random.random()
model.summary()
callbacks = [tf.keras.callbacks.EarlyStopping(patience=75, monitor='val_loss', restore_best_weights=True), tf.keras.callbacks.TensorBoard(log_dir='./logs/' + str(random_id))]
model.fit(X_train, y_train, epochs=2000, batch_size=240, validation_data=(X_valid, y_valid), callbacks=callbacks, shuffle=True)
model.evaluate(X_valid, y_valid)
print(y_valid[0][1], y_valid[1][1])
print(model.predict([[X_valid[1]]])) | code |
16133275/cell_5 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
onlyfiles = os.listdir('../input/utkface_aligned_cropped/UTKFace')
y = np.array([[[i.split('_')[0]], [i.split('_')[1]]] for i in onlyfiles])
X_data = []
for file in onlyfiles:
face = cv2.imread('../input/utkface_aligned_cropped/UTKFace/' + file, cv2.IMREAD_COLOR)
face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
face = cv2.resize(face, (32, 32))
X_data.append(face)
X_data = np.array(X_data)
X_data.shape
X = np.squeeze(X_data)
plt.imshow(X[1], interpolation='bicubic')
(plt.xticks([]), plt.yticks([]))
plt.show()
print(y[1]) | code |
50230470/cell_4 | [
"text_plain_output_1.png"
] | import random
def random_agent(observation, configuration):
return random.randrange(configuration.banditCount) | code |
50230470/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from kaggle_environments import make
from kaggle_environments import make
env = make('n-arm-bandit', debug=True)
env.reset()
env.run(['random_agent.py', 'my-sub-file.py'])
env.reset()
env.run(['my-sub-file.py', 'my-sub-file.py'])
env.render(mode='ipython', width=800, height=700) | code |
50230470/cell_2 | [
"text_plain_output_1.png"
] | !pip install kaggle-environments --upgrade | code |
50230470/cell_3 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import pandas as pd
basic_state = None
reward_full = 0
step_ending = None
def basic_mab(measurement, structure):
no_reward_step = 0.01
decay_rate = 0.99
global basic_state, reward_full, step_ending
if measurement.step == 0:
basic_state = [[1, 1] for i in range(structure['banditCount'])]
else:
reward_final = measurement['given_reward'] - reward_full
reward_full = measurement['given_reward']
player = int(step_ending == measurement.lastActions[1])
if reward_final > 0:
basic_state[measurement.lastActions[player]][0] += reward_final
else:
basic_state[measurement.lastActions[player]][1] += no_reward_step
basic_state[measurement.lastActions[0]][0] = (basic_state[measurement.lastActions[0]][0] - 1) * decay_rate + 1
basic_state[measurement.lastActions[1]][0] = (basic_state[measurement.lastActions[1]][0] - 1) * decay_rate + 1
best_proba = -1
agent_optimal = None
for k in range(structure['banditCount']):
proba = np.random.beta(basic_state[k][0], basic_state[k][1])
if proba > best_proba:
best_proba = proba
agent_optimal = k
step_ending = agent_optimal
return agent_optimal | code |
50230470/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from kaggle_environments import make
from kaggle_environments import make
env = make('n-arm-bandit', debug=True)
env.reset()
env.run(['random_agent.py', 'my-sub-file.py'])
env.render(mode='ipython', width=800, height=700) | code |
17124444/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
data.head() | code |
17124444/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
y = data['Outcome']
feature_data = data.drop('Outcome', axis=1)
scaled_features = StandardScaler().fit_transform(feature_data.values)
scaled_data = pd.DataFrame(scaled_features, index=feature_data.index, columns=feature_data.columns)
scaled_data.head() | code |
17124444/cell_11 | [
"text_html_output_1.png"
] | import operator
dic = {}
dic = {1: 1.2, 2: 1.56, 3: 5.2, 6: 7.1, 4: 2.7}
sorted(dic.items(), key=lambda item: item[1], reverse=True) | code |
17124444/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17124444/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
y = data['Outcome']
feature_data = data.drop('Outcome', axis=1)
scaled_features = StandardScaler().fit_transform(feature_data.values)
scaled_data = pd.DataFrame(scaled_features, index=feature_data.index, columns=feature_data.columns)
train_scaled = pd.concat([scaled_data, y], axis=1)
train_scaled.head() | code |
17124444/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/diabetes.csv')
y = data['Outcome']
feature_data = data.drop('Outcome', axis=1)
correlation = data.corr()
plt.figure(figsize=(10, 10))
sns.heatmap(correlation, annot=True)
plt.title('Correlation between different fearures') | code |
17124444/cell_14 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
y = data['Outcome']
feature_data = data.drop('Outcome', axis=1)
scaled_features = StandardScaler().fit_transform(feature_data.values)
scaled_data = pd.DataFrame(scaled_features, index=feature_data.index, columns=feature_data.columns)
train_scaled = pd.concat([scaled_data, y], axis=1)
def EuclideanD(d1, d2, length):
distance = 0
for l in range(length):
distance += np.square(d1[l] - d2[l])
return np.sqrt(distance)
def KNN(train, test, k):
result = []
length = test.shape[1]
for y in range(len(test)):
distances = {}
sort = {}
for x in range(len(train)):
dist = EuclideanD(test.iloc[y], train.iloc[x], length)
'\n basically we are iterating over the no. of features by calculating test.shape[1], and in EucliedeanD, we calculate distance \n for the data point by using Euclidean formula for the respective features of a data point and then return sigma(distance)\n '
distances[x] = dist
sorted_d = sorted(distances.items(), key=lambda item: item[1])
neighbours = []
for x in range(k):
neighbours.append(sorted_d[x][0])
majorityclassvotes = {}
for i in range(len(neighbours)):
response = train.iloc[neighbours[i]][-1]
if response in majorityclassvotes:
majorityclassvotes[response] += 1
else:
majorityclassvotes[response] = 1
majorityvotesorted = sorted(majorityclassvotes.items(), key=lambda item: item[1], reverse=True)
result.append(majorityvotesorted[0][0])
return result
test_data = [[0.42, 0.8, 0.25, 0.7, -0.12, 0.65, 0.36, 1.9], [-0.3, 0.5, 0.7, 0.2, -0.34, 0.86, 0.56, 2.8]]
test = pd.DataFrame(test_data)
k = 3
result = KNN(train_scaled, test, k)
from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(train_scaled.iloc[:, 0:8], train_scaled['Outcome'])
print(neigh.predict(test)) | code |
17124444/cell_12 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/diabetes.csv')
y = data['Outcome']
feature_data = data.drop('Outcome', axis=1)
scaled_features = StandardScaler().fit_transform(feature_data.values)
scaled_data = pd.DataFrame(scaled_features, index=feature_data.index, columns=feature_data.columns)
train_scaled = pd.concat([scaled_data, y], axis=1)
def EuclideanD(d1, d2, length):
distance = 0
for l in range(length):
distance += np.square(d1[l] - d2[l])
return np.sqrt(distance)
def KNN(train, test, k):
result = []
length = test.shape[1]
for y in range(len(test)):
distances = {}
sort = {}
for x in range(len(train)):
dist = EuclideanD(test.iloc[y], train.iloc[x], length)
'\n basically we are iterating over the no. of features by calculating test.shape[1], and in EucliedeanD, we calculate distance \n for the data point by using Euclidean formula for the respective features of a data point and then return sigma(distance)\n '
distances[x] = dist
sorted_d = sorted(distances.items(), key=lambda item: item[1])
neighbours = []
for x in range(k):
neighbours.append(sorted_d[x][0])
majorityclassvotes = {}
for i in range(len(neighbours)):
response = train.iloc[neighbours[i]][-1]
if response in majorityclassvotes:
majorityclassvotes[response] += 1
else:
majorityclassvotes[response] = 1
majorityvotesorted = sorted(majorityclassvotes.items(), key=lambda item: item[1], reverse=True)
result.append(majorityvotesorted[0][0])
return result
test_data = [[0.42, 0.8, 0.25, 0.7, -0.12, 0.65, 0.36, 1.9], [-0.3, 0.5, 0.7, 0.2, -0.34, 0.86, 0.56, 2.8]]
test = pd.DataFrame(test_data)
k = 3
result = KNN(train_scaled, test, k)
print(result) | code |
74054331/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.dtypes
df.isnull().sum()
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 5))
sns.barplot(x=df.columns, y=df.isnull().sum() / len(df))
plt.xticks(rotation=90)
plt.show() | code |
74054331/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.head() | code |
74054331/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.dtypes
df.isnull().sum()
import seaborn as sns
import matplotlib.pyplot as plt
plt.xticks(rotation=90)
df_plot = df.groupby(by=['State']).College_Name.nunique()
plt.figure(figsize=(20, 7))
plt.xticks(rotation=90)
sns.barplot(x=df_plot.index, y=df_plot) | code |
74054331/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74054331/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.dtypes
df.isnull().sum() | code |
74054331/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.dtypes
df.isnull().sum()
import seaborn as sns
import matplotlib.pyplot as plt
plt.xticks(rotation=90)
df_plot = df.groupby(by=['State']).College_Name.nunique()
plt.xticks(rotation=90)
g = df.groupby(by=['State', 'Stream']).College_Name.nunique()
g = g.reset_index()
g = g.rename(columns={'College_Name': 'Counts'})
sns.catplot(data=g, col='State', x='Stream', y='Counts', col_wrap=1, kind='bar', sharex=False, height=7, aspect=2) | code |
74054331/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/top-indian-colleges/College_data.csv')
df.dtypes | code |
73084093/cell_21 | [
"text_html_output_2.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from keras.layers import Dense
from keras.layers import Dense, LSTM
from keras.layers import Dropout
from keras.layers import LSTM
from keras.models import Sequential
from keras.models import Sequential
from pmdarima.arima import ADFTest
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
import keras
import numpy as np
import plotly.graph_objs as go
import yfinance as yf
import yfinance as yf
stock_name = 'AMD'
data = yf.download(stock_name, start='2020-03-26', end='2021-03-29')
from sklearn.preprocessing import MinMaxScaler
import math
import matplotlib.pyplot as plt
import keras
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import *
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers import Dense, LSTM
def lstm(stock_name, data):
data = data.filter(['Close'])
dataset = data.values
training_data_len = int(np.ceil(len(dataset) * 0.8))
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
train_data = scaled_data[0:int(training_data_len), :]
x_train = []
y_train = []
for i in range(60, len(train_data)):
x_train.append(train_data[i - 60:i, 0])
y_train.append(train_data[i, 0])
x_train, y_train = (np.array(x_train), np.array(y_train))
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
model = Sequential()
model.add(LSTM(128, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(Dropout(0.35))
model.add(LSTM(64, return_sequences=False))
model.add(Dropout(0.3))
model.add(Dense(25, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=1, epochs=21)
test_data = scaled_data[training_data_len - 60:, :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(60, len(test_data)):
x_test.append(test_data[i - 60:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
rmse = np.sqrt(np.mean((predictions - y_test) ** 2))
train = data[:training_data_len]
valid = data[training_data_len:]
train_gr = np.reshape(train, (203,))
train_gr = train_gr['Close']
valid_gr = np.reshape(valid, (50,))
valid_gr = valid_gr['Close']
preds_gr = np.reshape(predictions, (50,))
x_train = list(range(0, len(train_data)))
x_valid = list(range(len(train_data) - 1, len(dataset)))
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_train, y=train_gr, mode='lines+markers', marker=dict(size=4), name='train'))
fig.add_trace(go.Scatter(x=x_valid, y=valid_gr, mode='lines+markers', marker=dict(size=4), name='valid'))
fig.add_trace(go.Scatter(x=x_valid, y=preds_gr, mode='lines+markers', marker=dict(size=4), name='predictions'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} LSTM data', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
data_new = yf.download(stock_name, start='2021-03-01', end='2021-04-30')
data_new = data_new.filter(['Close'])
dataset = data_new.values
training_data_len = len(dataset)
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
test_data = scaled_data[training_data_len - len(data_new):, :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(20, len(test_data)):
x_test.append(test_data[i - 20:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
hist_data_new = yf.download(stock_name, start='2021-04-01', end='2021-05-04')
hist_data_new = hist_data_new.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)
hist_data_new = hist_data_new['Close']
hist_data_new = np.array(hist_data_new)
pred_lstm = model.predict(x_test)
pred_lstm = pred_lstm[:-1]
pred_lstm = scaler.inverse_transform(pred_lstm)
preds_gr = np.reshape(pred_lstm, (22,))
fig = go.Figure()
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=hist_data_new, mode='lines+markers', name='historical'))
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=preds_gr, mode='lines+markers', name='predictions'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} LSTM prediction', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
return (pred_lstm, rmse)
data_adf = data.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)
data_adf = data_adf['Close']
from pmdarima.arima import ADFTest
adf_test = ADFTest(alpha=0.05)
adf_test.should_diff(data_adf) | code |
73084093/cell_6 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | !pip install yfinance --quiet
!pip install pmdarima --quiet | code |
73084093/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | !pip install statsmodels==0.11.0rc1 --quiet
!pip install -Iv pulp==1.6.8 --quiet | code |
73084093/cell_16 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense, LSTM
from keras.layers import Dropout
from keras.layers import LSTM
from keras.models import Sequential
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
import keras
import numpy as np
import plotly.graph_objs as go
import yfinance as yf
import yfinance as yf
stock_name = 'AMD'
data = yf.download(stock_name, start='2020-03-26', end='2021-03-29')
from sklearn.preprocessing import MinMaxScaler
import math
import matplotlib.pyplot as plt
import keras
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import *
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers import Dense, LSTM
def lstm(stock_name, data):
data = data.filter(['Close'])
dataset = data.values
training_data_len = int(np.ceil(len(dataset) * 0.8))
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
train_data = scaled_data[0:int(training_data_len), :]
x_train = []
y_train = []
for i in range(60, len(train_data)):
x_train.append(train_data[i - 60:i, 0])
y_train.append(train_data[i, 0])
x_train, y_train = (np.array(x_train), np.array(y_train))
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
model = Sequential()
model.add(LSTM(128, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(Dropout(0.35))
model.add(LSTM(64, return_sequences=False))
model.add(Dropout(0.3))
model.add(Dense(25, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=1, epochs=21)
test_data = scaled_data[training_data_len - 60:, :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(60, len(test_data)):
x_test.append(test_data[i - 60:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
rmse = np.sqrt(np.mean((predictions - y_test) ** 2))
train = data[:training_data_len]
valid = data[training_data_len:]
train_gr = np.reshape(train, (203,))
train_gr = train_gr['Close']
valid_gr = np.reshape(valid, (50,))
valid_gr = valid_gr['Close']
preds_gr = np.reshape(predictions, (50,))
x_train = list(range(0, len(train_data)))
x_valid = list(range(len(train_data) - 1, len(dataset)))
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_train, y=train_gr, mode='lines+markers', marker=dict(size=4), name='train'))
fig.add_trace(go.Scatter(x=x_valid, y=valid_gr, mode='lines+markers', marker=dict(size=4), name='valid'))
fig.add_trace(go.Scatter(x=x_valid, y=preds_gr, mode='lines+markers', marker=dict(size=4), name='predictions'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} LSTM data', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
data_new = yf.download(stock_name, start='2021-03-01', end='2021-04-30')
data_new = data_new.filter(['Close'])
dataset = data_new.values
training_data_len = len(dataset)
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
test_data = scaled_data[training_data_len - len(data_new):, :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(20, len(test_data)):
x_test.append(test_data[i - 20:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
hist_data_new = yf.download(stock_name, start='2021-04-01', end='2021-05-04')
hist_data_new = hist_data_new.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)
hist_data_new = hist_data_new['Close']
hist_data_new = np.array(hist_data_new)
pred_lstm = model.predict(x_test)
pred_lstm = pred_lstm[:-1]
pred_lstm = scaler.inverse_transform(pred_lstm)
preds_gr = np.reshape(pred_lstm, (22,))
fig = go.Figure()
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=hist_data_new, mode='lines+markers', name='historical'))
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=preds_gr, mode='lines+markers', name='predictions'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} LSTM prediction', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
return (pred_lstm, rmse)
lstm_pred, lstm_rmse = lstm(stock_name, data) | code |
73084093/cell_17 | [
"text_plain_output_1.png"
] | print(lstm_pred.shape) | code |
73084093/cell_24 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense, LSTM
from keras.layers import Dropout
from keras.layers import LSTM
from keras.models import Sequential
from keras.models import Sequential
from pmdarima.arima import ADFTest
from pmdarima.arima import ADFTest
from pylab import rcParams
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
import keras
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import plotly.graph_objs as go
import pmdarima as pm
import warnings
import yfinance as yf
import yfinance as yf
stock_name = 'AMD'
data = yf.download(stock_name, start='2020-03-26', end='2021-03-29')
from sklearn.preprocessing import MinMaxScaler
import math
import matplotlib.pyplot as plt
import keras
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import *
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers import Dense, LSTM
def lstm(stock_name, data):
data = data.filter(['Close'])
dataset = data.values
training_data_len = int(np.ceil(len(dataset) * 0.8))
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
train_data = scaled_data[0:int(training_data_len), :]
x_train = []
y_train = []
for i in range(60, len(train_data)):
x_train.append(train_data[i - 60:i, 0])
y_train.append(train_data[i, 0])
x_train, y_train = (np.array(x_train), np.array(y_train))
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
model = Sequential()
model.add(LSTM(128, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(Dropout(0.35))
model.add(LSTM(64, return_sequences=False))
model.add(Dropout(0.3))
model.add(Dense(25, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=1, epochs=21)
test_data = scaled_data[training_data_len - 60:, :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(60, len(test_data)):
x_test.append(test_data[i - 60:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
rmse = np.sqrt(np.mean((predictions - y_test) ** 2))
train = data[:training_data_len]
valid = data[training_data_len:]
train_gr = np.reshape(train, (203,))
train_gr = train_gr['Close']
valid_gr = np.reshape(valid, (50,))
valid_gr = valid_gr['Close']
preds_gr = np.reshape(predictions, (50,))
x_train = list(range(0, len(train_data)))
x_valid = list(range(len(train_data) - 1, len(dataset)))
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_train, y=train_gr, mode='lines+markers', marker=dict(size=4), name='train'))
fig.add_trace(go.Scatter(x=x_valid, y=valid_gr, mode='lines+markers', marker=dict(size=4), name='valid'))
fig.add_trace(go.Scatter(x=x_valid, y=preds_gr, mode='lines+markers', marker=dict(size=4), name='predictions'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} LSTM data', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
data_new = yf.download(stock_name, start='2021-03-01', end='2021-04-30')
data_new = data_new.filter(['Close'])
dataset = data_new.values
training_data_len = len(dataset)
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
test_data = scaled_data[training_data_len - len(data_new):, :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(20, len(test_data)):
x_test.append(test_data[i - 20:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
hist_data_new = yf.download(stock_name, start='2021-04-01', end='2021-05-04')
hist_data_new = hist_data_new.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)
hist_data_new = hist_data_new['Close']
hist_data_new = np.array(hist_data_new)
pred_lstm = model.predict(x_test)
pred_lstm = pred_lstm[:-1]
pred_lstm = scaler.inverse_transform(pred_lstm)
preds_gr = np.reshape(pred_lstm, (22,))
fig = go.Figure()
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=hist_data_new, mode='lines+markers', name='historical'))
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=preds_gr, mode='lines+markers', name='predictions'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} LSTM prediction', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
return (pred_lstm, rmse)
data_adf = data.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)
data_adf = data_adf['Close']
from pmdarima.arima import ADFTest
adf_test = ADFTest(alpha=0.05)
adf_test.should_diff(data_adf)
import os
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pmdarima as pm
plt.style.use('fivethirtyeight')
from pylab import rcParams
rcParams['figure.figsize'] = (10, 6)
from statsmodels.tsa.arima_model import ARIMA
from pmdarima.arima import ADFTest
from pmdarima.datasets import load_wineind
import random
def arima(stock_name, data):
df_close = data['Close']
df_log = df_close
train_data, test_data = (df_log[3:int(len(df_log) * 0.9)], df_log[int(len(df_log) * 0.9):])
test_values = len(df_log) * 0.01 + 1.0
x_train = list(range(0, 224))
x_test = list(range(224, int(len(data))))
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_train, y=train_data, mode='lines+markers', marker=dict(size=4), name='train'))
fig.add_trace(go.Scatter(x=x_test, y=test_data, mode='lines+markers', marker=dict(size=4), name='test'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} ARIMA data', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
model = pm.auto_arima(df_log, start_p=0, d=None, start_q=0, max_p=5, max_d=5, max_q=5, start_P=0, D=1, start_Q=0, max_P=5, max_D=5, max_Q=5, m=7, seasonal=True, error_action='warn', trace=True, supress_warnings=True, stepwise=True, random_state=20, n_fits=50)
model.summary()
exo_data = data['Volume']
exo_data = exo_data[int(len(exo_data) * 0.9):]
preds = model.predict(n_periods=22, X=exo_data)
preds = np.vstack(preds)
hist_data = yf.download(stock_name, start='2021-04-01', end='2021-05-04')
hist_data = hist_data.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)
hist_data = hist_data['Close']
hist_data = np.array(hist_data)
rmse = np.sqrt(np.mean((preds - hist_data) ** 2))
preds_gr = np.reshape(preds, (22,))
fig = go.Figure()
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=hist_data, mode='lines+markers', name='historical'))
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=preds_gr, mode='lines+markers', name='predictions'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} ARIMA prediction', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
return (preds, rmse)
arima_pred, arima_rmse = arima(stock_name, data)
print(arima_pred.shape) | code |
73084093/cell_10 | [
"text_plain_output_1.png"
] | import yfinance as yf
import yfinance as yf
stock_name = 'AMD'
data = yf.download(stock_name, start='2020-03-26', end='2021-03-29') | code |
73084093/cell_27 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense, LSTM
from keras.layers import Dropout
from keras.layers import LSTM
from keras.models import Sequential
from keras.models import Sequential
from pmdarima.arima import ADFTest
from pmdarima.arima import ADFTest
from pylab import rcParams
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
from statsmodels.tsa.statespace.sarimax import SARIMAX
import keras
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import plotly.graph_objs as go
import pmdarima as pm
import warnings
import yfinance as yf
import yfinance as yf
stock_name = 'AMD'
data = yf.download(stock_name, start='2020-03-26', end='2021-03-29')
from sklearn.preprocessing import MinMaxScaler
import math
import matplotlib.pyplot as plt
import keras
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import *
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers import Dense, LSTM
def lstm(stock_name, data):
data = data.filter(['Close'])
dataset = data.values
training_data_len = int(np.ceil(len(dataset) * 0.8))
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
train_data = scaled_data[0:int(training_data_len), :]
x_train = []
y_train = []
for i in range(60, len(train_data)):
x_train.append(train_data[i - 60:i, 0])
y_train.append(train_data[i, 0])
x_train, y_train = (np.array(x_train), np.array(y_train))
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
model = Sequential()
model.add(LSTM(128, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(Dropout(0.35))
model.add(LSTM(64, return_sequences=False))
model.add(Dropout(0.3))
model.add(Dense(25, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=1, epochs=21)
test_data = scaled_data[training_data_len - 60:, :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(60, len(test_data)):
x_test.append(test_data[i - 60:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
rmse = np.sqrt(np.mean((predictions - y_test) ** 2))
train = data[:training_data_len]
valid = data[training_data_len:]
train_gr = np.reshape(train, (203,))
train_gr = train_gr['Close']
valid_gr = np.reshape(valid, (50,))
valid_gr = valid_gr['Close']
preds_gr = np.reshape(predictions, (50,))
x_train = list(range(0, len(train_data)))
x_valid = list(range(len(train_data) - 1, len(dataset)))
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_train, y=train_gr, mode='lines+markers', marker=dict(size=4), name='train'))
fig.add_trace(go.Scatter(x=x_valid, y=valid_gr, mode='lines+markers', marker=dict(size=4), name='valid'))
fig.add_trace(go.Scatter(x=x_valid, y=preds_gr, mode='lines+markers', marker=dict(size=4), name='predictions'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} LSTM data', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
data_new = yf.download(stock_name, start='2021-03-01', end='2021-04-30')
data_new = data_new.filter(['Close'])
dataset = data_new.values
training_data_len = len(dataset)
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
test_data = scaled_data[training_data_len - len(data_new):, :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(20, len(test_data)):
x_test.append(test_data[i - 20:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
hist_data_new = yf.download(stock_name, start='2021-04-01', end='2021-05-04')
hist_data_new = hist_data_new.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)
hist_data_new = hist_data_new['Close']
hist_data_new = np.array(hist_data_new)
pred_lstm = model.predict(x_test)
pred_lstm = pred_lstm[:-1]
pred_lstm = scaler.inverse_transform(pred_lstm)
preds_gr = np.reshape(pred_lstm, (22,))
fig = go.Figure()
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=hist_data_new, mode='lines+markers', name='historical'))
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=preds_gr, mode='lines+markers', name='predictions'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} LSTM prediction', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
return (pred_lstm, rmse)
data_adf = data.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)
data_adf = data_adf['Close']
from pmdarima.arima import ADFTest
adf_test = ADFTest(alpha=0.05)
adf_test.should_diff(data_adf)
import os
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pmdarima as pm
plt.style.use('fivethirtyeight')
from pylab import rcParams
rcParams['figure.figsize'] = (10, 6)
from statsmodels.tsa.arima_model import ARIMA
from pmdarima.arima import ADFTest
from pmdarima.datasets import load_wineind
import random
def arima(stock_name, data):
df_close = data['Close']
df_log = df_close
train_data, test_data = (df_log[3:int(len(df_log) * 0.9)], df_log[int(len(df_log) * 0.9):])
test_values = len(df_log) * 0.01 + 1.0
x_train = list(range(0, 224))
x_test = list(range(224, int(len(data))))
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_train, y=train_data, mode='lines+markers', marker=dict(size=4), name='train'))
fig.add_trace(go.Scatter(x=x_test, y=test_data, mode='lines+markers', marker=dict(size=4), name='test'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} ARIMA data', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
model = pm.auto_arima(df_log, start_p=0, d=None, start_q=0, max_p=5, max_d=5, max_q=5, start_P=0, D=1, start_Q=0, max_P=5, max_D=5, max_Q=5, m=7, seasonal=True, error_action='warn', trace=True, supress_warnings=True, stepwise=True, random_state=20, n_fits=50)
model.summary()
exo_data = data['Volume']
exo_data = exo_data[int(len(exo_data) * 0.9):]
preds = model.predict(n_periods=22, X=exo_data)
preds = np.vstack(preds)
hist_data = yf.download(stock_name, start='2021-04-01', end='2021-05-04')
hist_data = hist_data.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)
hist_data = hist_data['Close']
hist_data = np.array(hist_data)
rmse = np.sqrt(np.mean((preds - hist_data) ** 2))
preds_gr = np.reshape(preds, (22,))
fig = go.Figure()
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=hist_data, mode='lines+markers', name='historical'))
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=preds_gr, mode='lines+markers', name='predictions'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} ARIMA prediction', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
return (preds, rmse)
from statsmodels.tsa.statespace.sarimax import SARIMAX
data3 = data['Close']
train3_data, test3_data = (data3[3:int(len(data3) * 0.9)], data3[int(len(data3) * 0.9):])
x_train = list(range(0, 224))
x_test = list(range(224, int(len(data3))))
exo_data = data['Volume']
exo_data = exo_data[int(len(exo_data) * 0.9):]
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_train, y=train3_data, mode='lines+markers', marker=dict(size=4), name='train'))
fig.add_trace(go.Scatter(x=x_test, y=test3_data, mode='lines+markers', marker=dict(size=4), name='test'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} SARIMAX data', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
fig.show()
model = SARIMAX(train3_data, order=(3, 1, 2))
arima_model = model.fit(X=exo_data, disp=-1)
print(arima_model.summary())
preds3 = arima_model.predict(n_periods=22, alpha=0.05)
preds3 = np.vstack(preds3)
preds3 = preds3[-22:]
hist_data = yf.download(stock_name, start='2021-04-01', end='2021-05-04')
hist_data = hist_data.drop(['Open', 'High', 'Low', 'Adj Close', 'Volume'], axis=1)
hist_data = hist_data['Close']
hist_data = np.array(hist_data)
rmse = np.sqrt(np.mean((preds3 - hist_data) ** 2))
print(f'RMSE SARIMAX: {rmse}')
preds_gr = np.reshape(preds3, (22,))
fig = go.Figure()
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=hist_data, mode='lines+markers', name='historical'))
fig.add_trace(go.Scatter(x=list(range(0, 21)), y=preds_gr, mode='lines+markers', name='predictions'))
fig.update_layout(legend_orientation='h', legend=dict(x=0.5, xanchor='center'), title_text=f'{stock_name} SARIMAX prediction', title_x=0.5, xaxis_title='Timestep', yaxis_title='Stock price', margin=dict(l=0, r=0, t=30, b=0))
fig.show() | code |
130019203/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
X | code |
130019203/cell_23 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from tensorflow.keras.preprocessing.text import one_hot
import pandas as pd
import re
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
messages = X.copy()
import re
import nltk
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
ps = PorterStemmer()
corpus = []
for i in range(len(messages)):
review = re.sub('[^a-zA-Z]', ' ', messages['Headline'][i])
review = review.lower()
review = review.split()
review = [ps.stem(word) for word in review if not word in stopwords.words('english')]
review = ' '.join(review)
corpus.append(review)
voc_size = 5000
onehot_repr = [one_hot(words, voc_size) for words in corpus]
onehot_repr | code |
130019203/cell_33 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from sklearn.metrics import accuracy_score
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import one_hot
import numpy as np
import pandas as pd
import re
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
messages = X.copy()
import re
import nltk
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
ps = PorterStemmer()
corpus = []
for i in range(len(messages)):
review = re.sub('[^a-zA-Z]', ' ', messages['Headline'][i])
review = review.lower()
review = review.split()
review = [ps.stem(word) for word in review if not word in stopwords.words('english')]
review = ' '.join(review)
corpus.append(review)
voc_size = 5000
onehot_repr = [one_hot(words, voc_size) for words in corpus]
sent_length = 20
embedded_docs = pad_sequences(onehot_repr, padding='pre', maxlen=sent_length)
from tensorflow.keras.layers import Dropout
embedding_vector_features = 40
model = Sequential()
model.add(Embedding(voc_size, embedding_vector_features, input_length=sent_length))
model.add(Dropout(0.5))
model.add(LSTM(200))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
X_final = np.array(embedded_docs)
y_final = np.array(Y)
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=64)
y_pred = np.argmax(model.predict(X_test), axis=-1)
from sklearn.metrics import accuracy_score
print(f'{accuracy_score(y_test, y_pred) * 100}%') | code |
130019203/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.head() | code |
130019203/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape | code |
130019203/cell_18 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
import pandas as pd
import re
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
messages = X.copy()
import re
import nltk
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
ps = PorterStemmer()
corpus = []
for i in range(len(messages)):
review = re.sub('[^a-zA-Z]', ' ', messages['Headline'][i])
review = review.lower()
review = review.split()
review = [ps.stem(word) for word in review if not word in stopwords.words('english')]
review = ' '.join(review)
corpus.append(review)
corpus | code |
130019203/cell_32 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import one_hot
import pandas as pd
import re
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
messages = X.copy()
import re
import nltk
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
ps = PorterStemmer()
corpus = []
for i in range(len(messages)):
review = re.sub('[^a-zA-Z]', ' ', messages['Headline'][i])
review = review.lower()
review = review.split()
review = [ps.stem(word) for word in review if not word in stopwords.words('english')]
review = ' '.join(review)
corpus.append(review)
voc_size = 5000
onehot_repr = [one_hot(words, voc_size) for words in corpus]
sent_length = 20
embedded_docs = pad_sequences(onehot_repr, padding='pre', maxlen=sent_length)
from tensorflow.keras.layers import Dropout
embedding_vector_features = 40
model = Sequential()
model.add(Embedding(voc_size, embedding_vector_features, input_length=sent_length))
model.add(Dropout(0.5))
model.add(LSTM(200))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=64) | code |
130019203/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
df.info() | code |
130019203/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
messages = X.copy()
messages | code |
130019203/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
Y | code |
130019203/cell_27 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import one_hot
import pandas as pd
import re
df = pd.read_csv('/kaggle/input/covid19-india-news-headlines-for-nlp/raw_data.csv', usecols=['Headline', 'Sentiment', 'Description'])
df.shape
X = df.drop('Sentiment', axis=1)
Y = df['Sentiment']
messages = X.copy()
import re
import nltk
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
ps = PorterStemmer()
corpus = []
for i in range(len(messages)):
review = re.sub('[^a-zA-Z]', ' ', messages['Headline'][i])
review = review.lower()
review = review.split()
review = [ps.stem(word) for word in review if not word in stopwords.words('english')]
review = ' '.join(review)
corpus.append(review)
voc_size = 5000
onehot_repr = [one_hot(words, voc_size) for words in corpus]
sent_length = 20
embedded_docs = pad_sequences(onehot_repr, padding='pre', maxlen=sent_length)
embedded_docs | code |
74068297/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income']
data.isna().sum()
data = data.replace({'<=50K': 0, '>50K': 1})
data = data.replace({'<=50K': 0, '>50K': 1})
list_columns = []
list_categorical_col = []
list_numerical_col = []
def get_col(df: 'dataframe', type_descr: 'numpy') -> list:
"""
Function get list columns
Args:
type_descr
np.number, np.object -> return list with all columns
np.number -> return list numerical columns
np.object -> return list object columns
"""
try:
col = (df.describe(include=type_descr).columns) # pandas.core.indexes.base.Index
except ValueError:
print(f'Dataframe not contains {type_descr} columns !', end='\n')
else:
return col.tolist()
list_numerical_col = get_col(df=data, type_descr=np.number)
list_categorical_col = get_col(df=data, type_descr=np.object)
list_columns = get_col(df=data, type_descr=[np.object, np.number])
x = data[list_numerical_col].hist(figsize=[25, 22], density=True, bins=25, grid=False, color='orange', zorder=2, rwidth=0.9) | code |
74068297/cell_25 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income']
data.isna().sum()
data = data.replace({'<=50K': 0, '>50K': 1})
data = data.replace({'<=50K': 0, '>50K': 1})
data.corr() | code |
74068297/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income']
data.isna().sum()
data = data.replace({'<=50K': 0, '>50K': 1})
data = data.replace({'<=50K': 0, '>50K': 1})
sns.pairplot(data, kind='scatter', diag_kind='kde', corner=True, hue='income') | code |
74068297/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income']
data.isna().sum()
data = data.replace({'<=50K': 0, '>50K': 1})
data = data.replace({'<=50K': 0, '>50K': 1})
list_columns = []
list_categorical_col = []
list_numerical_col = []
def get_col(df: 'dataframe', type_descr: 'numpy') -> list:
"""
Function get list columns
Args:
type_descr
np.number, np.object -> return list with all columns
np.number -> return list numerical columns
np.object -> return list object columns
"""
try:
col = (df.describe(include=type_descr).columns) # pandas.core.indexes.base.Index
except ValueError:
print(f'Dataframe not contains {type_descr} columns !', end='\n')
else:
return col.tolist()
list_numerical_col = get_col(df=data, type_descr=np.number)
list_categorical_col = get_col(df=data, type_descr=np.object)
list_columns = get_col(df=data, type_descr=[np.object, np.number])
list_categorical_col | code |
74068297/cell_6 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape | code |
74068297/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income']
data.isna().sum()
data = data.replace({'<=50K': 0, '>50K': 1})
data = data.replace({'<=50K': 0, '>50K': 1})
data.corr()
sns.heatmap(data.corr(), annot=True, cmap='PiYG') | code |
74068297/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income']
data.isna().sum()
data = data.replace({'<=50K': 0, '>50K': 1})
data = data.replace({'<=50K': 0, '>50K': 1})
list_columns = []
list_categorical_col = []
list_numerical_col = []
def get_col(df: 'dataframe', type_descr: 'numpy') -> list:
"""
Function get list columns
Args:
type_descr
np.number, np.object -> return list with all columns
np.number -> return list numerical columns
np.object -> return list object columns
"""
try:
col = (df.describe(include=type_descr).columns) # pandas.core.indexes.base.Index
except ValueError:
print(f'Dataframe not contains {type_descr} columns !', end='\n')
else:
return col.tolist()
list_numerical_col = get_col(df=data, type_descr=np.number)
list_categorical_col = get_col(df=data, type_descr=np.object)
list_columns = get_col(df=data, type_descr=[np.object, np.number])
list_numerical_col | code |
74068297/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74068297/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.head() | code |
74068297/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income']
data.isna().sum()
data = data.replace({'<=50K': 0, '>50K': 1})
data = data.replace({'<=50K': 0, '>50K': 1})
data.corr()
eda_percentage = data['income'].value_counts(normalize=True).rename_axis('income').reset_index(name='Percentage')
ax = sns.barplot(x='income', y='Percentage', data=eda_percentage.head(10), palette='RdGy_r')
eda_percentage | code |
74068297/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income']
data.isna().sum()
data = data.replace({'<=50K': 0, '>50K': 1})
data = data.replace({'<=50K': 0, '>50K': 1})
data.corr()
eda_percentage = data['income'].value_counts(normalize = True).rename_axis('income').reset_index(name = 'Percentage')
ax = sns.barplot(x = 'income', y = 'Percentage', data = eda_percentage.head(10), palette='RdGy_r')
eda_percentage
plt.figure(figsize=(12, 6))
order_list = ['Less than 18', '19-30', '31-40', '41-50', '51-60', '61-70', 'Greater than 70']
sns.countplot(data['age_group'], hue=data['income'], palette='autumn_r', order=order_list)
plt.title('Income of Individuals of Different Age Groups', fontsize=18)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.legend(fontsize=16) | code |
74068297/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv', na_values='?')
data.shape
data.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income']
data.isna().sum() | code |
90153288/cell_21 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for i in df.columns if i not in ['popularity', 'tempo', 'instrumentalness']])
df_mr.tempo = df_mr.tempo.apply(lambda row: str(row).replace('?', '0') if row == '?' else str(row))
df_mr.tempo = df_mr.tempo.apply(pd.to_numeric, errors='coerce')
df_mr = df_mr.dropna()
x = df_mr[['instrumentalness', 'tempo']]
y = df_mr['popularity']
from sklearn import linear_model
regr = linear_model.LinearRegression()
regr.fit(x, y)
regr.coef_ | code |
90153288/cell_23 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for i in df.columns if i not in ['popularity', 'tempo', 'instrumentalness']])
df_mr.tempo = df_mr.tempo.apply(lambda row: str(row).replace('?', '0') if row == '?' else str(row))
df_mr.tempo = df_mr.tempo.apply(pd.to_numeric, errors='coerce')
df_mr = df_mr.dropna()
x = df_mr[['instrumentalness', 'tempo']]
y = df_mr['popularity']
from sklearn import linear_model
regr = linear_model.LinearRegression()
regr.fit(x, y)
regr.coef_
regr.predict([[0.0045, 55.25]]) | code |
90153288/cell_30 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/prediction-of-music-genre/music_genre.csv')
import seaborn as sns
import matplotlib.pyplot as plt
df_mr = df.drop(columns=[i for i in df.columns if i not in ['popularity', 'tempo', 'instrumentalness']])
df_mr.tempo = df_mr.tempo.apply(lambda row: str(row).replace('?', '0') if row == '?' else str(row))
df_mr.tempo = df_mr.tempo.apply(pd.to_numeric, errors='coerce')
df_mr = df_mr.dropna()
x = df_mr[['instrumentalness', 'tempo']]
y = df_mr['popularity']
def fit(x, a):
f = a[2] * x * x + a[1] * x + a[0]
return f
def grad(x, a):
g = 2 * a[2] * x + a[1]
return g
x = df_mr[['tempo', 'instrumentalness']]
y = df_mr['popularity']
f = fit(x, y)
x = df_mr[['tempo']]
y = df_mr['popularity']
def find_theta(X, y):
m = X.shape[0]
X = np.append(X, np.ones((m, 1)), axis=1)
theta = np.dot(np.linalg.inv(np.dot(X.T, X)), np.dot(X.T, y))
return theta
def predict(X):
X = np.append(X, np.ones((X.shape[0], 1)), axis=1)
preds = np.dot(X, theta)
return preds
theta = find_theta(x, y)
print(theta)
preds = predict(x)
fig = plt.figure(figsize=(8, 6))
plt.plot(x, y, 'y.')
plt.plot(x, preds, 'c-')
plt.xlabel('Input')
plt.ylabel('target') | code |
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