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stringlengths 13
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sequencelengths 1
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|---|---|---|---|
128017162/cell_28 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
test = pd.read_csv('/kaggle/input/hackerearth/dataset/test.csv')
preds_list = test['Image']
preds_list | code |
128017162/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
num = len(train['Class'].unique())
print('Total Labels : ', str(num)) | code |
128017162/cell_15 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
import pandas as pd
import visualkeras as vk
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
num = len(train['Class'].unique())
model = Sequential()
model.add(Conv2D(16, kernel_size=(5, 5), activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(32, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(num, activation='softmax'))
model.summary()
vk.layered_view(model, legend=True) | code |
128017162/cell_16 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import plot_model, load_img, to_categorical
import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
num = len(train['Class'].unique())
model = Sequential()
model.add(Conv2D(16, kernel_size=(5, 5), activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(32, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(num, activation='softmax'))
model.summary()
plot_model(model) | code |
128017162/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
train['Class'].unique() | code |
128017162/cell_14 | [
"text_html_output_1.png"
] | from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
num = len(train['Class'].unique())
model = Sequential()
model.add(Conv2D(16, kernel_size=(5, 5), activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(32, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(num, activation='softmax'))
model.summary() | code |
128017162/cell_22 | [
"image_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import pandas as pd
train_dir = '/kaggle/input/hackerearth/dataset/Train Images'
test_dir = '/kaggle/input/hackerearth/dataset/Test Images'
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
num = len(train['Class'].unique())
datagen = ImageDataGenerator(rescale=1.0 / 255.0, validation_split=0.2)
train_it = datagen.flow_from_dataframe(train, directory=train_dir, x_col='Image', y_col='Class', target_size=(150, 150), class_mode='categorical', batch_size=32, subset='training', shuffle=True)
valid_it = datagen.flow_from_dataframe(train, directory=train_dir, x_col='Image', y_col='Class', target_size=(150, 150), class_mode='categorical', subset='validation', batch_size=32, shuffle=True)
model = Sequential()
model.add(Conv2D(16, kernel_size=(5, 5), activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(32, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(num, activation='softmax'))
model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
class myCallback(Callback):
def on_epoch_end(self, epoch, logs={}):
if logs.get('accuracy') >= 0.98:
self.model.stop_training = True
callback = myCallback()
hist = model.fit(train_it, epochs=1500, validation_data=valid_it, callbacks=callback)
plt.figure()
plt.plot(hist.history['loss'], label='Train Loss', color='black')
plt.plot(hist.history['val_loss'], label='Validation Loss', color='mediumvioletred', linestyle='dashed', markeredgecolor='purple', markeredgewidth=2)
plt.title('Model Loss', color='darkred', size=13)
plt.legend()
plt.show() | code |
128017162/cell_27 | [
"image_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import pandas as pd
train_dir = '/kaggle/input/hackerearth/dataset/Train Images'
test_dir = '/kaggle/input/hackerearth/dataset/Test Images'
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
test = pd.read_csv('/kaggle/input/hackerearth/dataset/test.csv')
num = len(train['Class'].unique())
datagen = ImageDataGenerator(rescale=1.0 / 255.0, validation_split=0.2)
train_it = datagen.flow_from_dataframe(train, directory=train_dir, x_col='Image', y_col='Class', target_size=(150, 150), class_mode='categorical', batch_size=32, subset='training', shuffle=True)
valid_it = datagen.flow_from_dataframe(train, directory=train_dir, x_col='Image', y_col='Class', target_size=(150, 150), class_mode='categorical', subset='validation', batch_size=32, shuffle=True)
model = Sequential()
model.add(Conv2D(16, kernel_size=(5, 5), activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(32, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(num, activation='softmax'))
model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
class myCallback(Callback):
def on_epoch_end(self, epoch, logs={}):
if logs.get('accuracy') >= 0.98:
self.model.stop_training = True
callback = myCallback()
hist = model.fit(train_it, epochs=1500, validation_data=valid_it, callbacks=callback)
test_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
SIZE = (150, 150, 3)
test_generator = test_datagen.flow_from_dataframe(test, directory=test_dir, x_col='Image', y_col=None, class_mode=None, target_size=(150, 150))
preds = model.predict(test_generator)
y_pred = [np.argmax(probas) for probas in preds]
len(y_pred) | code |
128017162/cell_37 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import pandas as pd
train_dir = '/kaggle/input/hackerearth/dataset/Train Images'
test_dir = '/kaggle/input/hackerearth/dataset/Test Images'
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
test = pd.read_csv('/kaggle/input/hackerearth/dataset/test.csv')
num = len(train['Class'].unique())
datagen = ImageDataGenerator(rescale=1.0 / 255.0, validation_split=0.2)
train_it = datagen.flow_from_dataframe(train, directory=train_dir, x_col='Image', y_col='Class', target_size=(150, 150), class_mode='categorical', batch_size=32, subset='training', shuffle=True)
valid_it = datagen.flow_from_dataframe(train, directory=train_dir, x_col='Image', y_col='Class', target_size=(150, 150), class_mode='categorical', subset='validation', batch_size=32, shuffle=True)
model = Sequential()
model.add(Conv2D(16, kernel_size=(5, 5), activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(32, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(num, activation='softmax'))
model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
class myCallback(Callback):
def on_epoch_end(self, epoch, logs={}):
if logs.get('accuracy') >= 0.98:
self.model.stop_training = True
callback = myCallback()
hist = model.fit(train_it, epochs=1500, validation_data=valid_it, callbacks=callback)
test_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
SIZE = (150, 150, 3)
test_generator = test_datagen.flow_from_dataframe(test, directory=test_dir, x_col='Image', y_col=None, class_mode=None, target_size=(150, 150))
preds = model.predict(test_generator)
y_pred = [np.argmax(probas) for probas in preds]
preds_list = test['Image']
preds_list
labels = train['Class'].unique()
prediction = []
for i in y_pred:
if i == 0:
prediction.append(labels[0])
elif i == 1:
prediction.append(labels[1])
elif i == 2:
prediction.append(labels[2])
else:
prediction.append(labels[3])
results = pd.DataFrame({'Image': preds_list, 'Class': prediction})
results['Class'].unique() | code |
128017162/cell_36 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import pandas as pd
train_dir = '/kaggle/input/hackerearth/dataset/Train Images'
test_dir = '/kaggle/input/hackerearth/dataset/Test Images'
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
test = pd.read_csv('/kaggle/input/hackerearth/dataset/test.csv')
num = len(train['Class'].unique())
datagen = ImageDataGenerator(rescale=1.0 / 255.0, validation_split=0.2)
train_it = datagen.flow_from_dataframe(train, directory=train_dir, x_col='Image', y_col='Class', target_size=(150, 150), class_mode='categorical', batch_size=32, subset='training', shuffle=True)
valid_it = datagen.flow_from_dataframe(train, directory=train_dir, x_col='Image', y_col='Class', target_size=(150, 150), class_mode='categorical', subset='validation', batch_size=32, shuffle=True)
model = Sequential()
model.add(Conv2D(16, kernel_size=(5, 5), activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(32, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, kernel_size=(5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(num, activation='softmax'))
model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
class myCallback(Callback):
def on_epoch_end(self, epoch, logs={}):
if logs.get('accuracy') >= 0.98:
self.model.stop_training = True
callback = myCallback()
hist = model.fit(train_it, epochs=1500, validation_data=valid_it, callbacks=callback)
test_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
SIZE = (150, 150, 3)
test_generator = test_datagen.flow_from_dataframe(test, directory=test_dir, x_col='Image', y_col=None, class_mode=None, target_size=(150, 150))
preds = model.predict(test_generator)
y_pred = [np.argmax(probas) for probas in preds]
preds_list = test['Image']
preds_list
labels = train['Class'].unique()
prediction = []
for i in y_pred:
if i == 0:
prediction.append(labels[0])
elif i == 1:
prediction.append(labels[1])
elif i == 2:
prediction.append(labels[2])
else:
prediction.append(labels[3])
results = pd.DataFrame({'Image': preds_list, 'Class': prediction})
results | code |
122265193/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/air-concentration-for-the-chernobyl-disaster/data.csv')
data.info() | code |
122265193/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/air-concentration-for-the-chernobyl-disaster/data.csv')
m = data['I_131_(Bq/m3)'].str.contains('L|\\?', regex=True, na=False)
data.loc[m, 'I_131_(Bq/m3)'] = None
m = data['Cs_134_(Bq/m3)'].str.contains('N|\\?', regex=True)
data.loc[m, 'Cs_134_(Bq/m3)'] = None
m = data['Cs_137_(Bq/m3)'].str.contains('N|\\?', regex=True)
data.loc[m, 'Cs_137_(Bq/m3)'] = None
data.info() | code |
122265193/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/air-concentration-for-the-chernobyl-disaster/data.csv')
print(sorted(data['I_131_(Bq/m3)'].unique())[-5:])
print(sorted(data['Cs_134_(Bq/m3)'].unique())[-5:])
print(sorted(data['Cs_137_(Bq/m3)'].unique())[-5:]) | code |
122265193/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/air-concentration-for-the-chernobyl-disaster/data.csv')
data | code |
122265193/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/air-concentration-for-the-chernobyl-disaster/data.csv')
m = data['I_131_(Bq/m3)'].str.contains('L|\\?', regex=True, na=False)
data.loc[m, 'I_131_(Bq/m3)'] = None
m = data['Cs_134_(Bq/m3)'].str.contains('N|\\?', regex=True)
data.loc[m, 'Cs_134_(Bq/m3)'] = None
m = data['Cs_137_(Bq/m3)'].str.contains('N|\\?', regex=True)
data.loc[m, 'Cs_137_(Bq/m3)'] = None
data.Ville.unique() | code |
16154606/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pytorch_pretrained_bert import BertConfig
from pytorch_pretrained_bert import BertTokenizer, BertForSequenceClassification, BertAdam
from tqdm import tqdm
import numpy as np
import pandas as pd
import torch
device = torch.device('cuda')
def convert_lines(example, max_seq_length, tokenizer):
max_seq_length -= 2
all_tokens = []
longer = 0
for text in tqdm(example):
tokens_a = tokenizer.tokenize(text)
if len(tokens_a) > max_seq_length:
tokens_a = tokens_a[:max_seq_length]
longer += 1
one_token = tokenizer.convert_tokens_to_ids(['[CLS]'] + tokens_a + ['[SEP]']) + [0] * (max_seq_length - len(tokens_a))
all_tokens.append(one_token)
return np.array(all_tokens)
MAX_SEQUENCE_LENGTH = 200
SEED = 42
BATCH_SIZE = 32
BERT_MODEL_PATH = '../input/bert-pretrained-models/uncased_l-12_h-768_a-12/uncased_L-12_H-768_A-12/'
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
bert_config = BertConfig('../input/bertinference/bert_config.json')
tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_PATH, cache_dir=None, do_lower_case=True)
BERT_SMALL_PATH = '../input/bert-pretrained-models/uncased_l-12_h-768_a-12/uncased_L-12_H-768_A-12/'
BERT_LARGE_PATH = '../input/bert-pretrained-models/uncased_l-24_h-1024_a-16/uncased_L-24_H-1024_A-16/'
test_df = pd.read_csv('../input/jigsaw-unintended-bias-in-toxicity-classification/test.csv')
test_df['comment_text'] = test_df['comment_text'].astype(str)
X_test = convert_lines(test_df['comment_text'].fillna('DUMMY_VALUE'), MAX_SEQUENCE_LENGTH, tokenizer)
model = BertForSequenceClassification(bert_config, num_labels=1)
model.load_state_dict(torch.load('../input/bertinference/pytorch_bert_6.bin'))
model.to(device)
for param in model.parameters():
param.requires_grad = False
model.eval()
test_preds = np.zeros(len(X_test))
test = torch.utils.data.TensorDataset(torch.tensor(X_test, dtype=torch.long))
test_loader = torch.utils.data.DataLoader(test, batch_size=32, shuffle=False)
tk0 = tqdm(test_loader)
for i, (x_batch,) in enumerate(tk0):
pred = model(x_batch.to(device), attention_mask=(x_batch > 0).to(device), labels=None)
test_preds[i * 32:(i + 1) * 32] = pred[:, 0].detach().cpu().squeeze().numpy()
test_pred = torch.sigmoid(torch.tensor(test_preds)).numpy().ravel() | code |
16154606/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pytorch_pretrained_bert import BertConfig
from pytorch_pretrained_bert import BertTokenizer, BertForSequenceClassification, BertAdam
from tqdm import tqdm
import numpy as np
import pandas as pd
import torch
device = torch.device('cuda')
def convert_lines(example, max_seq_length, tokenizer):
max_seq_length -= 2
all_tokens = []
longer = 0
for text in tqdm(example):
tokens_a = tokenizer.tokenize(text)
if len(tokens_a) > max_seq_length:
tokens_a = tokens_a[:max_seq_length]
longer += 1
one_token = tokenizer.convert_tokens_to_ids(['[CLS]'] + tokens_a + ['[SEP]']) + [0] * (max_seq_length - len(tokens_a))
all_tokens.append(one_token)
return np.array(all_tokens)
MAX_SEQUENCE_LENGTH = 200
SEED = 42
BATCH_SIZE = 32
BERT_MODEL_PATH = '../input/bert-pretrained-models/uncased_l-12_h-768_a-12/uncased_L-12_H-768_A-12/'
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
bert_config = BertConfig('../input/bertinference/bert_config.json')
tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_PATH, cache_dir=None, do_lower_case=True)
BERT_SMALL_PATH = '../input/bert-pretrained-models/uncased_l-12_h-768_a-12/uncased_L-12_H-768_A-12/'
BERT_LARGE_PATH = '../input/bert-pretrained-models/uncased_l-24_h-1024_a-16/uncased_L-24_H-1024_A-16/'
test_df = pd.read_csv('../input/jigsaw-unintended-bias-in-toxicity-classification/test.csv')
test_df['comment_text'] = test_df['comment_text'].astype(str)
X_test = convert_lines(test_df['comment_text'].fillna('DUMMY_VALUE'), MAX_SEQUENCE_LENGTH, tokenizer) | code |
16154606/cell_7 | [
"text_plain_output_1.png"
] | from pytorch_pretrained_bert import BertConfig
from pytorch_pretrained_bert import BertTokenizer, BertForSequenceClassification, BertAdam
from tqdm import tqdm
import numpy as np
import torch
device = torch.device('cuda')
def convert_lines(example, max_seq_length, tokenizer):
max_seq_length -= 2
all_tokens = []
longer = 0
for text in tqdm(example):
tokens_a = tokenizer.tokenize(text)
if len(tokens_a) > max_seq_length:
tokens_a = tokens_a[:max_seq_length]
longer += 1
one_token = tokenizer.convert_tokens_to_ids(['[CLS]'] + tokens_a + ['[SEP]']) + [0] * (max_seq_length - len(tokens_a))
all_tokens.append(one_token)
return np.array(all_tokens)
MAX_SEQUENCE_LENGTH = 200
SEED = 42
BATCH_SIZE = 32
BERT_MODEL_PATH = '../input/bert-pretrained-models/uncased_l-12_h-768_a-12/uncased_L-12_H-768_A-12/'
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
bert_config = BertConfig('../input/bertinference/bert_config.json')
tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_PATH, cache_dir=None, do_lower_case=True)
BERT_SMALL_PATH = '../input/bert-pretrained-models/uncased_l-12_h-768_a-12/uncased_L-12_H-768_A-12/'
BERT_LARGE_PATH = '../input/bert-pretrained-models/uncased_l-24_h-1024_a-16/uncased_L-24_H-1024_A-16/'
model = BertForSequenceClassification(bert_config, num_labels=1)
model.load_state_dict(torch.load('../input/bertinference/pytorch_bert_6.bin'))
model.to(device)
for param in model.parameters():
param.requires_grad = False
model.eval() | code |
73095926/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import model_selection
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.income.value_counts()
from sklearn import model_selection
df['kfold'] = -1
df = df.sample(frac=1).reset_index(drop=True)
y = df.income.values
kf = model_selection.StratifiedKFold(n_splits=5)
for fold, (train_, valid_) in enumerate(kf.split(X=df, y=y)):
df.loc[valid_, 'kfold'] = fold
df.to_csv('./adult_folds.csv', index=False)
df_fold = pd.read_csv('./adult_folds.csv')
df_train = df[df.kfold != 0].reset_index(drop=True)
df_train.income.isnull().sum() | code |
73095926/cell_2 | [
"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/adult-census-income/adult.csv')
df.income.value_counts() | code |
73095926/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 |
73095926/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import metrics
from sklearn import model_selection
from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import xgboost as xgb
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.income.value_counts()
from sklearn import model_selection
df['kfold'] = -1
df = df.sample(frac=1).reset_index(drop=True)
y = df.income.values
kf = model_selection.StratifiedKFold(n_splits=5)
for fold, (train_, valid_) in enumerate(kf.split(X=df, y=y)):
df.loc[valid_, 'kfold'] = fold
df.to_csv('./adult_folds.csv', index=False)
df_fold = pd.read_csv('./adult_folds.csv')
df_train = df[df.kfold != 0].reset_index(drop=True)
df_train.income.isnull().sum()
from sklearn import linear_model
from sklearn import metrics
from sklearn import preprocessing
def run(fold):
df = pd.read_csv('./adult_folds.csv')
num_cols = ['fnlwgt', 'age', 'capital.gain', 'capital.loss', 'hours.per.week']
df = df.drop(num_cols, axis=1)
target_mapping = {'<=50K': 0, '>50K': 1}
df.loc[:, 'income'] = df.income.map(target_mapping)
features = [f for f in df.columns if f not in ('kfold', 'income')]
for col in features:
df.loc[:, col] = df[col].astype(str).fillna('NONE')
df_train = df[df.kfold != fold].reset_index(drop=True)
df_valid = df[df.kfold == fold].reset_index(drop=True)
ohe = preprocessing.OneHotEncoder()
All_data = pd.concat([df_train[features], df_valid[features]], axis=0)
ohe.fit(All_data[features])
x_train = ohe.transform(df_train[features])
x_valid = ohe.transform(df_valid[features])
model = linear_model.LogisticRegression(solver='liblinear')
model.fit(x_train, df_train.income.values)
valid_preds = model.predict_proba(x_valid)[:, 1]
auc = metrics.roc_auc_score(df_valid.income.values, valid_preds)
if __name__ == '__main__':
for fold_ in range(5):
run(fold_)
import warnings
warnings.filterwarnings('ignore')
import xgboost as xgb
def Xgboost_fold(fold):
df = pd.read_csv('./adult_folds.csv')
num_cols = ['fnlwgt', 'age', 'capital.gain', 'capital.loss', 'hours.per.week']
df = df.drop(num_cols, axis=1)
target_mapping = {'<=50K': 0, '>50K': 1}
df.loc[:, 'income'] = df.income.map(target_mapping)
features = [f for f in df.columns if f not in ('kfold', 'income')]
for col in features:
df.loc[:, col] = df[col].astype(str).fillna('NONE')
for col in features:
lbl = preprocessing.LabelEncoder()
lbl.fit(df[col])
df.loc[:, col] = lbl.transform(df[col])
df_train = df[df.kfold != fold].reset_index(drop=True)
df_valid = df[df.kfold == fold].reset_index(drop=True)
x_train = df_train[features].values
x_valid = df_valid[features].values
model = xgb.XGBClassifier(n_jobs=-1)
model.fit(x_train, df_train.income.values)
valid_preds = model.predict_proba(x_valid)[:, 1]
auc = metrics.roc_auc_score(df_valid.income.values, valid_preds)
if __name__ == '__main__':
for fold_ in range(5):
Xgboost_fold(fold_)
def Xgboost_fold(fold):
df = pd.read_csv('./adult_folds.csv')
num_cols = ['fnlwgt', 'age', 'capital.gain', 'capital.loss', 'hours.per.week']
target_mapping = {'<=50K': 0, '>50K': 1}
df.loc[:, 'income'] = df.income.map(target_mapping)
features = [f for f in df.columns if f not in ('kfold', 'income')]
for col in features:
if col not in num_cols:
df.loc[:, col] = df[col].astype(str).fillna('NONE')
for col in features:
if col not in num_cols:
lbl = preprocessing.LabelEncoder()
lbl.fit(df[col])
df.loc[:, col] = lbl.transform(df[col])
df_train = df[df.kfold != fold].reset_index(drop=True)
df_valid = df[df.kfold == fold].reset_index(drop=True)
x_train = df_train[features].values
x_valid = df_valid[features].values
model = xgb.XGBClassifier(n_jobs=-1)
model.fit(x_train, df_train.income.values)
valid_preds = model.predict_proba(x_valid)[:, 1]
auc = metrics.roc_auc_score(df_valid.income.values, valid_preds)
print(f'Fold = {fold}, AUC = {auc}')
if __name__ == '__main__':
for fold_ in range(5):
Xgboost_fold(fold_) | code |
73095926/cell_3 | [
"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/adult-census-income/adult.csv')
df.income.value_counts()
df.head() | code |
73095926/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn import metrics
from sklearn import model_selection
from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.income.value_counts()
from sklearn import model_selection
df['kfold'] = -1
df = df.sample(frac=1).reset_index(drop=True)
y = df.income.values
kf = model_selection.StratifiedKFold(n_splits=5)
for fold, (train_, valid_) in enumerate(kf.split(X=df, y=y)):
df.loc[valid_, 'kfold'] = fold
df.to_csv('./adult_folds.csv', index=False)
df_fold = pd.read_csv('./adult_folds.csv')
df_train = df[df.kfold != 0].reset_index(drop=True)
df_train.income.isnull().sum()
from sklearn import linear_model
from sklearn import metrics
from sklearn import preprocessing
def run(fold):
df = pd.read_csv('./adult_folds.csv')
num_cols = ['fnlwgt', 'age', 'capital.gain', 'capital.loss', 'hours.per.week']
df = df.drop(num_cols, axis=1)
target_mapping = {'<=50K': 0, '>50K': 1}
df.loc[:, 'income'] = df.income.map(target_mapping)
features = [f for f in df.columns if f not in ('kfold', 'income')]
for col in features:
df.loc[:, col] = df[col].astype(str).fillna('NONE')
df_train = df[df.kfold != fold].reset_index(drop=True)
df_valid = df[df.kfold == fold].reset_index(drop=True)
ohe = preprocessing.OneHotEncoder()
All_data = pd.concat([df_train[features], df_valid[features]], axis=0)
ohe.fit(All_data[features])
x_train = ohe.transform(df_train[features])
x_valid = ohe.transform(df_valid[features])
model = linear_model.LogisticRegression(solver='liblinear')
model.fit(x_train, df_train.income.values)
valid_preds = model.predict_proba(x_valid)[:, 1]
auc = metrics.roc_auc_score(df_valid.income.values, valid_preds)
print(f'Fold = {fold}, AUC = {auc}')
if __name__ == '__main__':
for fold_ in range(5):
run(fold_) | code |
73095926/cell_12 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn import metrics
from sklearn import model_selection
from sklearn import preprocessing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import xgboost as xgb
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.income.value_counts()
from sklearn import model_selection
df['kfold'] = -1
df = df.sample(frac=1).reset_index(drop=True)
y = df.income.values
kf = model_selection.StratifiedKFold(n_splits=5)
for fold, (train_, valid_) in enumerate(kf.split(X=df, y=y)):
df.loc[valid_, 'kfold'] = fold
df.to_csv('./adult_folds.csv', index=False)
df_fold = pd.read_csv('./adult_folds.csv')
df_train = df[df.kfold != 0].reset_index(drop=True)
df_train.income.isnull().sum()
from sklearn import linear_model
from sklearn import metrics
from sklearn import preprocessing
def run(fold):
df = pd.read_csv('./adult_folds.csv')
num_cols = ['fnlwgt', 'age', 'capital.gain', 'capital.loss', 'hours.per.week']
df = df.drop(num_cols, axis=1)
target_mapping = {'<=50K': 0, '>50K': 1}
df.loc[:, 'income'] = df.income.map(target_mapping)
features = [f for f in df.columns if f not in ('kfold', 'income')]
for col in features:
df.loc[:, col] = df[col].astype(str).fillna('NONE')
df_train = df[df.kfold != fold].reset_index(drop=True)
df_valid = df[df.kfold == fold].reset_index(drop=True)
ohe = preprocessing.OneHotEncoder()
All_data = pd.concat([df_train[features], df_valid[features]], axis=0)
ohe.fit(All_data[features])
x_train = ohe.transform(df_train[features])
x_valid = ohe.transform(df_valid[features])
model = linear_model.LogisticRegression(solver='liblinear')
model.fit(x_train, df_train.income.values)
valid_preds = model.predict_proba(x_valid)[:, 1]
auc = metrics.roc_auc_score(df_valid.income.values, valid_preds)
if __name__ == '__main__':
for fold_ in range(5):
run(fold_)
import warnings
warnings.filterwarnings('ignore')
import xgboost as xgb
def Xgboost_fold(fold):
df = pd.read_csv('./adult_folds.csv')
num_cols = ['fnlwgt', 'age', 'capital.gain', 'capital.loss', 'hours.per.week']
df = df.drop(num_cols, axis=1)
target_mapping = {'<=50K': 0, '>50K': 1}
df.loc[:, 'income'] = df.income.map(target_mapping)
features = [f for f in df.columns if f not in ('kfold', 'income')]
for col in features:
df.loc[:, col] = df[col].astype(str).fillna('NONE')
for col in features:
lbl = preprocessing.LabelEncoder()
lbl.fit(df[col])
df.loc[:, col] = lbl.transform(df[col])
df_train = df[df.kfold != fold].reset_index(drop=True)
df_valid = df[df.kfold == fold].reset_index(drop=True)
x_train = df_train[features].values
x_valid = df_valid[features].values
model = xgb.XGBClassifier(n_jobs=-1)
model.fit(x_train, df_train.income.values)
valid_preds = model.predict_proba(x_valid)[:, 1]
auc = metrics.roc_auc_score(df_valid.income.values, valid_preds)
print(f'Fold = {fold}, AUC = {auc}')
if __name__ == '__main__':
for fold_ in range(5):
Xgboost_fold(fold_) | code |
73095926/cell_5 | [
"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/adult-census-income/adult.csv')
df.income.value_counts()
df['income'].isnull().sum() | code |
90157865/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import plotly.express as px
vaults = pd.read_csv('../input/psolvaults/output.csv')
solPrice = 80
vaults['debt'] = vaults['debtAmount'] / 10 ** vaults['decimal']
vaults['debtValue'] = vaults['debt'] * solPrice
vaults['collateral'] = vaults['collateralAmount'] / 10 ** vaults['decimal']
vaults['collateralValue'] = vaults['collateral'] * solPrice
vaults | code |
73088459/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,auc,classification_report,confusion_matrix,mean_squared_error, precision_score, recall_score,roc_curve
from sklearn.model_selection import cross_val_score,cross_val_predict,cross_validate,train_test_split,GridSearchCV,KFold,RepeatedKFold,learning_curve,RandomizedSearchCV,StratifiedKFold
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col='id')
full_df = train.copy()
fulltest_df = test.copy()
preprocessing1 = 'none'
preprocessing2 = 'none'
y = train.target
train.drop(['target'], axis=1, inplace=True)
preprocessing1 = 'ordinal encoding'
Cfeatures = [col for col in useful_features if 'cat' in col]
ordinal_encoder = OrdinalEncoder()
train[Cfeatures] = ordinal_encoder.fit_transform(train[Cfeatures])
test[Cfeatures] = ordinal_encoder.transform(test[Cfeatures])
preprocessing1 = 'ordinal & ione-hot encoding'
Nfeatures = [cname for cname in train.columns if train[cname].dtype in ['int64', 'float64']]
Cfeatures = [col for col in useful_features if 'cat' in col]
low_card_columns = [cname for cname in train.columns if train[cname].nunique() < 10 and train[cname].dtype == 'object']
high_card_columns = [cname for cname in train.columns if train[cname].nunique() >= 10 and train[cname].dtype == 'object']
Xtrain = train.copy()
Xtest = test.copy()
ordinal_encoder = OrdinalEncoder()
train[high_card_columns] = ordinal_encoder.fit_transform(train[high_card_columns])
test[high_card_columns] = ordinal_encoder.transform(test[high_card_columns])
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(train[low_card_columns]))
OH_cols_test = pd.DataFrame(OH_encoder.transform(test[low_card_columns]))
OH_cols_train.index = train.index
OH_cols_test.index = test.index
all_cols = OH_cols_train.columns
new_cols = [i for i in all_cols if isinstance(i, (int, float))]
OH_cols_train = OH_cols_train[new_cols].add_prefix('cat_encode_')
OH_cols_test = OH_cols_test[new_cols].add_prefix('cat_encode_')
num_X_train = train.drop(low_card_columns, axis=1)
num_X_test = test.drop(low_card_columns, axis=1)
train = pd.concat([num_X_train, OH_cols_train], axis=1)
test = pd.concat([num_X_test, OH_cols_test], axis=1)
useful_features = [c for c in train.columns if c not in ('id', 'target', 'kfold')]
Nfeatures = [cname for cname in train.columns if train[cname].dtype in ['int64', 'float64']]
train = train[Nfeatures]
test = test[Nfeatures]
X_train, X_valid, y_train, y_valid = train_test_split(train, y, train_size=0.025, test_size=0.025, random_state=0)
train_size = 0.025
test = test
PreprocessPerformanced_df = pd.DataFrame(columns=['modelname', 'preprocessing1', 'preprocessing2', 'datashape', 'trainsize', 'mean_squared_error'])
modelname = 'XGBRegressor'
preprocessing1 = 'none'
preprocessing2 = 'none'
modelname = 'XGBRegressor'
trainingshape = train.shape
model = XGBRegressor(n_estimators=1000, learning_rate=0.03, random_state=1, n_jobs=2)
model.fit(X_train, y_train, early_stopping_rounds=20, eval_set=[(X_valid, y_valid)], verbose=False)
preds_valid = model.predict(X_valid)
mse_score = mean_squared_error(y_valid, preds_valid, squared=False)
PreprocessPerformanced_df = PreprocessPerformanced_df.append({'modelname': modelname, 'preprocessing1': preprocessing1, 'preprocessing2': preprocessing2, 'datshape': trainingshape, 'trainsize': train_size, 'mean_squared_error': mse_score}, ignore_index=True)
predictions = model.predict(test)
output = pd.DataFrame({'Id': test.index, 'target': predictions})
output.to_csv('basic_xgboost_submission.csv', index=False)
PreprocessPerformanced_df = PreprocessPerformanced_df.sort_values('mean_squared_error', ascending=True)
print(PreprocessPerformanced_df)
filename = 'preprocessing_' + time.strftime('%Y_%m_%d_%H_%M') + '.csv'
output = PreprocessPerformanced_df
print('\nreview saved as', filename) | code |
73088459/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from scipy.stats import norm, randint
from math import ceil
import time
import os
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import seaborn as sns
from catboost import CatBoostRegressor
from sklearn.compose import ColumnTransformer
from sklearn.datasets import make_regression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier, GradientBoostingClassifier, GradientBoostingRegressor, RandomForestClassifier, RandomForestRegressor, VotingClassifier
from sklearn.feature_selection import mutual_info_regression
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression, Perceptron, SGDClassifier, LogisticRegression, PassiveAggressiveClassifier, RidgeClassifierCV, Ridge
from sklearn.metrics import accuracy_score, auc, classification_report, confusion_matrix, mean_squared_error, precision_score, recall_score, roc_curve
from sklearn.metrics import mean_squared_error as MSE
from sklearn.model_selection import cross_val_score, cross_val_predict, cross_validate, train_test_split, GridSearchCV, KFold, RepeatedKFold, learning_curve, RandomizedSearchCV, StratifiedKFold
from sklearn.multiclass import OneVsRestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from sklearn.svm import SVC, LinearSVC, SVR
from sklearn.tree import DecisionTreeClassifier
from sklearn import ensemble, linear_model, neighbors, svm, tree, model_selection, preprocessing
from sklearn import utils
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
import lightgbm as lgbm
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73088459/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col='id')
full_df = train.copy()
fulltest_df = test.copy()
print('Data Import Complete')
preprocessing1 = 'none'
preprocessing2 = 'none'
y = train.target
train.drop(['target'], axis=1, inplace=True)
print('Target data separated') | code |
73088459/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,auc,classification_report,confusion_matrix,mean_squared_error, precision_score, recall_score,roc_curve
from sklearn.model_selection import cross_val_score,cross_val_predict,cross_validate,train_test_split,GridSearchCV,KFold,RepeatedKFold,learning_curve,RandomizedSearchCV,StratifiedKFold
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col='id')
full_df = train.copy()
fulltest_df = test.copy()
preprocessing1 = 'none'
preprocessing2 = 'none'
y = train.target
train.drop(['target'], axis=1, inplace=True)
preprocessing1 = 'ordinal encoding'
Cfeatures = [col for col in useful_features if 'cat' in col]
ordinal_encoder = OrdinalEncoder()
train[Cfeatures] = ordinal_encoder.fit_transform(train[Cfeatures])
test[Cfeatures] = ordinal_encoder.transform(test[Cfeatures])
preprocessing1 = 'ordinal & ione-hot encoding'
Nfeatures = [cname for cname in train.columns if train[cname].dtype in ['int64', 'float64']]
Cfeatures = [col for col in useful_features if 'cat' in col]
low_card_columns = [cname for cname in train.columns if train[cname].nunique() < 10 and train[cname].dtype == 'object']
high_card_columns = [cname for cname in train.columns if train[cname].nunique() >= 10 and train[cname].dtype == 'object']
Xtrain = train.copy()
Xtest = test.copy()
ordinal_encoder = OrdinalEncoder()
train[high_card_columns] = ordinal_encoder.fit_transform(train[high_card_columns])
test[high_card_columns] = ordinal_encoder.transform(test[high_card_columns])
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(train[low_card_columns]))
OH_cols_test = pd.DataFrame(OH_encoder.transform(test[low_card_columns]))
OH_cols_train.index = train.index
OH_cols_test.index = test.index
all_cols = OH_cols_train.columns
new_cols = [i for i in all_cols if isinstance(i, (int, float))]
OH_cols_train = OH_cols_train[new_cols].add_prefix('cat_encode_')
OH_cols_test = OH_cols_test[new_cols].add_prefix('cat_encode_')
num_X_train = train.drop(low_card_columns, axis=1)
num_X_test = test.drop(low_card_columns, axis=1)
train = pd.concat([num_X_train, OH_cols_train], axis=1)
test = pd.concat([num_X_test, OH_cols_test], axis=1)
useful_features = [c for c in train.columns if c not in ('id', 'target', 'kfold')]
Nfeatures = [cname for cname in train.columns if train[cname].dtype in ['int64', 'float64']]
train = train[Nfeatures]
test = test[Nfeatures]
X_train, X_valid, y_train, y_valid = train_test_split(train, y, train_size=0.025, test_size=0.025, random_state=0)
train_size = 0.025
test = test
PreprocessPerformanced_df = pd.DataFrame(columns=['modelname', 'preprocessing1', 'preprocessing2', 'datashape', 'trainsize', 'mean_squared_error'])
modelname = 'XGBRegressor'
preprocessing1 = 'none'
preprocessing2 = 'none'
modelname = 'XGBRegressor'
trainingshape = train.shape
print(trainingshape)
model = XGBRegressor(n_estimators=1000, learning_rate=0.03, random_state=1, n_jobs=2)
model.fit(X_train, y_train, early_stopping_rounds=20, eval_set=[(X_valid, y_valid)], verbose=False)
preds_valid = model.predict(X_valid)
mse_score = mean_squared_error(y_valid, preds_valid, squared=False)
print(mse_score)
PreprocessPerformanced_df = PreprocessPerformanced_df.append({'modelname': modelname, 'preprocessing1': preprocessing1, 'preprocessing2': preprocessing2, 'datshape': trainingshape, 'trainsize': train_size, 'mean_squared_error': mse_score}, ignore_index=True)
print('Results added to comparison file')
predictions = model.predict(test)
output = pd.DataFrame({'Id': test.index, 'target': predictions})
output.to_csv('basic_xgboost_submission.csv', index=False)
print('basic xgboost submission complete') | code |
73088459/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col='id')
full_df = train.copy()
fulltest_df = test.copy()
preprocessing1 = 'none'
preprocessing2 = 'none'
y = train.target
train.drop(['target'], axis=1, inplace=True)
preprocessing1 = 'ordinal encoding'
Cfeatures = [col for col in useful_features if 'cat' in col]
print(Cfeatures)
ordinal_encoder = OrdinalEncoder()
train[Cfeatures] = ordinal_encoder.fit_transform(train[Cfeatures])
test[Cfeatures] = ordinal_encoder.transform(test[Cfeatures])
print('All category columns converted to ordinal') | code |
73088459/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col='id')
full_df = train.copy()
fulltest_df = test.copy()
preprocessing1 = 'none'
preprocessing2 = 'none'
y = train.target
train.drop(['target'], axis=1, inplace=True)
preprocessing1 = 'ordinal encoding'
Cfeatures = [col for col in useful_features if 'cat' in col]
ordinal_encoder = OrdinalEncoder()
train[Cfeatures] = ordinal_encoder.fit_transform(train[Cfeatures])
test[Cfeatures] = ordinal_encoder.transform(test[Cfeatures])
preprocessing1 = 'ordinal & ione-hot encoding'
Nfeatures = [cname for cname in train.columns if train[cname].dtype in ['int64', 'float64']]
Cfeatures = [col for col in useful_features if 'cat' in col]
low_card_columns = [cname for cname in train.columns if train[cname].nunique() < 10 and train[cname].dtype == 'object']
high_card_columns = [cname for cname in train.columns if train[cname].nunique() >= 10 and train[cname].dtype == 'object']
Xtrain = train.copy()
Xtest = test.copy()
ordinal_encoder = OrdinalEncoder()
train[high_card_columns] = ordinal_encoder.fit_transform(train[high_card_columns])
test[high_card_columns] = ordinal_encoder.transform(test[high_card_columns])
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(train[low_card_columns]))
OH_cols_test = pd.DataFrame(OH_encoder.transform(test[low_card_columns]))
OH_cols_train.index = train.index
OH_cols_test.index = test.index
all_cols = OH_cols_train.columns
new_cols = [i for i in all_cols if isinstance(i, (int, float))]
OH_cols_train = OH_cols_train[new_cols].add_prefix('cat_encode_')
OH_cols_test = OH_cols_test[new_cols].add_prefix('cat_encode_')
num_X_train = train.drop(low_card_columns, axis=1)
num_X_test = test.drop(low_card_columns, axis=1)
train = pd.concat([num_X_train, OH_cols_train], axis=1)
test = pd.concat([num_X_test, OH_cols_test], axis=1)
PreprocessPerformanced_df = pd.DataFrame(columns=['modelname', 'preprocessing1', 'preprocessing2', 'datashape', 'trainsize', 'mean_squared_error'])
modelname = 'XGBRegressor'
preprocessing1 = 'none'
preprocessing2 = 'none'
print('Dataframe created') | code |
73088459/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score,cross_val_predict,cross_validate,train_test_split,GridSearchCV,KFold,RepeatedKFold,learning_curve,RandomizedSearchCV,StratifiedKFold
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col='id')
full_df = train.copy()
fulltest_df = test.copy()
preprocessing1 = 'none'
preprocessing2 = 'none'
y = train.target
train.drop(['target'], axis=1, inplace=True)
preprocessing1 = 'ordinal encoding'
Cfeatures = [col for col in useful_features if 'cat' in col]
ordinal_encoder = OrdinalEncoder()
train[Cfeatures] = ordinal_encoder.fit_transform(train[Cfeatures])
test[Cfeatures] = ordinal_encoder.transform(test[Cfeatures])
preprocessing1 = 'ordinal & ione-hot encoding'
Nfeatures = [cname for cname in train.columns if train[cname].dtype in ['int64', 'float64']]
Cfeatures = [col for col in useful_features if 'cat' in col]
low_card_columns = [cname for cname in train.columns if train[cname].nunique() < 10 and train[cname].dtype == 'object']
high_card_columns = [cname for cname in train.columns if train[cname].nunique() >= 10 and train[cname].dtype == 'object']
Xtrain = train.copy()
Xtest = test.copy()
ordinal_encoder = OrdinalEncoder()
train[high_card_columns] = ordinal_encoder.fit_transform(train[high_card_columns])
test[high_card_columns] = ordinal_encoder.transform(test[high_card_columns])
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(train[low_card_columns]))
OH_cols_test = pd.DataFrame(OH_encoder.transform(test[low_card_columns]))
OH_cols_train.index = train.index
OH_cols_test.index = test.index
all_cols = OH_cols_train.columns
new_cols = [i for i in all_cols if isinstance(i, (int, float))]
OH_cols_train = OH_cols_train[new_cols].add_prefix('cat_encode_')
OH_cols_test = OH_cols_test[new_cols].add_prefix('cat_encode_')
num_X_train = train.drop(low_card_columns, axis=1)
num_X_test = test.drop(low_card_columns, axis=1)
train = pd.concat([num_X_train, OH_cols_train], axis=1)
test = pd.concat([num_X_test, OH_cols_test], axis=1)
useful_features = [c for c in train.columns if c not in ('id', 'target', 'kfold')]
Nfeatures = [cname for cname in train.columns if train[cname].dtype in ['int64', 'float64']]
train = train[Nfeatures]
test = test[Nfeatures]
X_train, X_valid, y_train, y_valid = train_test_split(train, y, train_size=0.025, test_size=0.025, random_state=0)
train_size = 0.025
test = test
print('Data split') | code |
73088459/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col='id')
full_df = train.copy()
fulltest_df = test.copy()
preprocessing1 = 'none'
preprocessing2 = 'none'
y = train.target
train.drop(['target'], axis=1, inplace=True)
preprocessing1 = 'ordinal encoding'
Cfeatures = [col for col in useful_features if 'cat' in col]
ordinal_encoder = OrdinalEncoder()
train[Cfeatures] = ordinal_encoder.fit_transform(train[Cfeatures])
test[Cfeatures] = ordinal_encoder.transform(test[Cfeatures])
preprocessing1 = 'ordinal & ione-hot encoding'
Nfeatures = [cname for cname in train.columns if train[cname].dtype in ['int64', 'float64']]
Cfeatures = [col for col in useful_features if 'cat' in col]
low_card_columns = [cname for cname in train.columns if train[cname].nunique() < 10 and train[cname].dtype == 'object']
high_card_columns = [cname for cname in train.columns if train[cname].nunique() >= 10 and train[cname].dtype == 'object']
Xtrain = train.copy()
Xtest = test.copy()
ordinal_encoder = OrdinalEncoder()
train[high_card_columns] = ordinal_encoder.fit_transform(train[high_card_columns])
test[high_card_columns] = ordinal_encoder.transform(test[high_card_columns])
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(train[low_card_columns]))
OH_cols_test = pd.DataFrame(OH_encoder.transform(test[low_card_columns]))
OH_cols_train.index = train.index
OH_cols_test.index = test.index
all_cols = OH_cols_train.columns
new_cols = [i for i in all_cols if isinstance(i, (int, float))]
OH_cols_train = OH_cols_train[new_cols].add_prefix('cat_encode_')
OH_cols_test = OH_cols_test[new_cols].add_prefix('cat_encode_')
num_X_train = train.drop(low_card_columns, axis=1)
num_X_test = test.drop(low_card_columns, axis=1)
train = pd.concat([num_X_train, OH_cols_train], axis=1)
test = pd.concat([num_X_test, OH_cols_test], axis=1)
useful_features = [c for c in train.columns if c not in ('id', 'target', 'kfold')]
Nfeatures = [cname for cname in train.columns if train[cname].dtype in ['int64', 'float64']]
train = train[Nfeatures]
test = test[Nfeatures]
print('Categorical data removed') | code |
2008154/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
img_size = 64
channel_size = 1
print('Training shape:', X_train.shape)
print(X_train.shape[0], 'sample,', X_train.shape[1], 'x', X_train.shape[2], 'size grayscale image.\n')
print('Test shape:', X_test.shape)
print(X_test.shape[0], 'sample,', X_test.shape[1], 'x', X_test.shape[2], 'size grayscale image.\n')
print('Examples:')
n = 10
plt.figure(figsize=(20, 4))
for i in range(1, n + 1):
ax = plt.subplot(1, n, i)
plt.imshow(X_train[i].reshape(img_size, img_size))
plt.gray()
plt.axis('off') | code |
2008154/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
print(check_output(['ls', '../input/Sign-language-digits-dataset']).decode('utf8')) | code |
90108999/cell_21 | [
"text_plain_output_1.png"
] | y_test_temp = y_test.reshape(1, len(y_test))[0]
print(type(y_test_temp))
print(y_test_temp.shape) | code |
90108999/cell_9 | [
"image_output_1.png"
] | import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
img_arr = cv2.imread(os.path.join(path, img), 0)
resized_arr = cv2.resize(img_arr, (image_size, image_size))
data.append([resized_arr, class_num])
return data
val = data_loader('../input/chest-xray-pneumonia/chest_xray/val')
test = data_loader('../input/chest-xray-pneumonia/chest_xray/test')
train = data_loader('../input/chest-xray-pneumonia/chest_xray/train')
def normalize_list(train):
for pair in train:
pair[0] = np.array(pair[0]) / 255
return train
train = normalize_list(train)
test = normalize_list(test)
val = normalize_list(val)
train[0] | code |
90108999/cell_6 | [
"image_output_1.png"
] | import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
img_arr = cv2.imread(os.path.join(path, img), 0)
resized_arr = cv2.resize(img_arr, (image_size, image_size))
data.append([resized_arr, class_num])
return data
val = data_loader('../input/chest-xray-pneumonia/chest_xray/val')
test = data_loader('../input/chest-xray-pneumonia/chest_xray/test')
train = data_loader('../input/chest-xray-pneumonia/chest_xray/train')
plt.figure(figsize=(6, 6))
plt.imshow(np.asnumpy(train[2][0])) | code |
90108999/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
img_arr = cv2.imread(os.path.join(path, img), 0)
resized_arr = cv2.resize(img_arr, (image_size, image_size))
data.append([resized_arr, class_num])
return data
val = data_loader('../input/chest-xray-pneumonia/chest_xray/val')
test = data_loader('../input/chest-xray-pneumonia/chest_xray/test')
train = data_loader('../input/chest-xray-pneumonia/chest_xray/train')
def normalize_list(train):
for pair in train:
pair[0] = np.array(pair[0]) / 255
return train
train = normalize_list(train)
test = normalize_list(test)
val = normalize_list(val)
def make_x_y(data):
X = []
Y = []
for pair in data:
X.append(pair[0])
Y.append(pair[1])
return (np.array(X), np.array(Y))
x_train, y_train = make_x_y(train)
x_test, y_test = make_x_y(test)
x_val, y_val = make_x_y(val)
x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
cuml_model = cuRFC(max_features=1.0, n_bins=8, n_estimators=40)
cuml_model.fit(x_train.astype('float32'), y_train.astype('float32'))
y_rfc_predict = cuml_model.predict(x_test.astype('float32'))
y_test_temp = y_test.reshape(1, len(y_test))[0]
y_np_test = np.asnumpy(y_test_temp)
y_np_rfc_predict = np.asnumpy(y_rfc_predict)
print('classification report of model: \n')
print(classification_report(y_np_test, y_np_rfc_predict, target_names=labels)) | code |
90108999/cell_19 | [
"text_plain_output_1.png"
] | x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
cuml_model = cuRFC(max_features=1.0, n_bins=8, n_estimators=40)
cuml_model.fit(x_train.astype('float32'), y_train.astype('float32'))
y_rfc_predict = cuml_model.predict(x_test.astype('float32'))
print(type(y_rfc_predict))
print(y_rfc_predict.shape) | code |
90108999/cell_18 | [
"text_plain_output_1.png"
] | x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
cuml_model = cuRFC(max_features=1.0, n_bins=8, n_estimators=40)
cuml_model.fit(x_train.astype('float32'), y_train.astype('float32')) | code |
90108999/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from cuml.metrics.confusion_matrix import confusion_matrix
from sklearn import metrics
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
import seaborn as sns
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
img_arr = cv2.imread(os.path.join(path, img), 0)
resized_arr = cv2.resize(img_arr, (image_size, image_size))
data.append([resized_arr, class_num])
return data
val = data_loader('../input/chest-xray-pneumonia/chest_xray/val')
test = data_loader('../input/chest-xray-pneumonia/chest_xray/test')
train = data_loader('../input/chest-xray-pneumonia/chest_xray/train')
def normalize_list(train):
for pair in train:
pair[0] = np.array(pair[0]) / 255
return train
train = normalize_list(train)
test = normalize_list(test)
val = normalize_list(val)
def make_x_y(data):
X = []
Y = []
for pair in data:
X.append(pair[0])
Y.append(pair[1])
return (np.array(X), np.array(Y))
x_train, y_train = make_x_y(train)
x_test, y_test = make_x_y(test)
x_val, y_val = make_x_y(val)
x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
cuml_model = cuRFC(max_features=1.0, n_bins=8, n_estimators=40)
cuml_model.fit(x_train.astype('float32'), y_train.astype('float32'))
y_rfc_predict = cuml_model.predict(x_test.astype('float32'))
y_test_temp = y_test.reshape(1, len(y_test))[0]
y_np_test = np.asnumpy(y_test_temp)
y_np_rfc_predict = np.asnumpy(y_rfc_predict)
print("confusion matrix of model: \n")
cmap = confusion_matrix(y_np_test, y_np_rfc_predict)
plt.figure(figsize = (4, 4), dpi = 150)
hm = sns.heatmap(data=cmap,annot=True,fmt='g')
pred = cuml_model.predict(x_test)
pred = np.asnumpy(pred)
fpr, tpr, threshold = metrics.roc_curve(y_np_test, pred)
roc_auc = metrics.auc(fpr, tpr)
plt.figure(figsize=(6, 4), dpi=150)
plt.title('ROC curve')
plt.plot(fpr, tpr, 'b', label='AUC = %0.2f' % roc_auc)
plt.legend(loc='lower right')
plt.plot([0, 1], [0, 1], 'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show() | code |
90108999/cell_35 | [
"text_plain_output_1.png"
] | from cuml.metrics.confusion_matrix import confusion_matrix
from sklearn import metrics
from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import roc_auc_score
import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
import seaborn as sns
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
img_arr = cv2.imread(os.path.join(path, img), 0)
resized_arr = cv2.resize(img_arr, (image_size, image_size))
data.append([resized_arr, class_num])
return data
val = data_loader('../input/chest-xray-pneumonia/chest_xray/val')
test = data_loader('../input/chest-xray-pneumonia/chest_xray/test')
train = data_loader('../input/chest-xray-pneumonia/chest_xray/train')
def normalize_list(train):
for pair in train:
pair[0] = np.array(pair[0]) / 255
return train
train = normalize_list(train)
test = normalize_list(test)
val = normalize_list(val)
def make_x_y(data):
X = []
Y = []
for pair in data:
X.append(pair[0])
Y.append(pair[1])
return (np.array(X), np.array(Y))
x_train, y_train = make_x_y(train)
x_test, y_test = make_x_y(test)
x_val, y_val = make_x_y(val)
x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
cuml_model = cuRFC(max_features=1.0, n_bins=8, n_estimators=40)
cuml_model.fit(x_train.astype('float32'), y_train.astype('float32'))
y_rfc_predict = cuml_model.predict(x_test.astype('float32'))
y_test_temp = y_test.reshape(1, len(y_test))[0]
y_np_test = np.asnumpy(y_test_temp)
y_np_rfc_predict = np.asnumpy(y_rfc_predict)
print("confusion matrix of model: \n")
cmap = confusion_matrix(y_np_test, y_np_rfc_predict)
plt.figure(figsize = (4, 4), dpi = 150)
hm = sns.heatmap(data=cmap,annot=True,fmt='g')
pred = cuml_model.predict(x_test)
pred = np.asnumpy(pred)
fpr, tpr, threshold = metrics.roc_curve(y_np_test, pred)
roc_auc = metrics.auc(fpr, tpr)
plt.xlim([0, 1])
plt.ylim([0, 1])
accuracy = accuracy_score(y_np_test, y_np_rfc_predict)
precision = precision_score(y_np_test, y_np_rfc_predict)
recall = recall_score(y_np_test, y_np_rfc_predict)
f1 = f1_score(y_np_test, y_np_rfc_predict)
roc = roc_auc_score(y_np_test, y_np_rfc_predict)
value = [accuracy, precision, recall, f1, roc]
labels = ['Accuarcy', 'Precision', 'Recall', 'F1', 'ROC Score']
plt.figure(figsize=(6, 4), dpi=150)
plt.bar(labels, value)
plt.title('Metrics')
plt.show() | code |
90108999/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
img_arr = cv2.imread(os.path.join(path, img), 0)
resized_arr = cv2.resize(img_arr, (image_size, image_size))
data.append([resized_arr, class_num])
return data
val = data_loader('../input/chest-xray-pneumonia/chest_xray/val')
test = data_loader('../input/chest-xray-pneumonia/chest_xray/test')
train = data_loader('../input/chest-xray-pneumonia/chest_xray/train')
def normalize_list(train):
for pair in train:
pair[0] = np.array(pair[0]) / 255
return train
train = normalize_list(train)
test = normalize_list(test)
val = normalize_list(val)
def make_x_y(data):
X = []
Y = []
for pair in data:
X.append(pair[0])
Y.append(pair[1])
return (np.array(X), np.array(Y))
x_train, y_train = make_x_y(train)
x_test, y_test = make_x_y(test)
x_val, y_val = make_x_y(val)
x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
cuml_model = cuRFC(max_features=1.0, n_bins=8, n_estimators=40)
cuml_model.fit(x_train.astype('float32'), y_train.astype('float32'))
y_rfc_predict = cuml_model.predict(x_test.astype('float32'))
y_test_temp = y_test.reshape(1, len(y_test))[0]
y_np_test = np.asnumpy(y_test_temp)
y_np_rfc_predict = np.asnumpy(y_rfc_predict)
accu = accuracy_score(y_np_test, y_np_rfc_predict)
print('accuracy of model is: %f' % accu) | code |
90108999/cell_14 | [
"text_plain_output_1.png"
] | x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
print(x_train.shape)
print(x_test.shape)
print(x_val.shape) | code |
90108999/cell_22 | [
"text_plain_output_1.png"
] | import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
img_arr = cv2.imread(os.path.join(path, img), 0)
resized_arr = cv2.resize(img_arr, (image_size, image_size))
data.append([resized_arr, class_num])
return data
val = data_loader('../input/chest-xray-pneumonia/chest_xray/val')
test = data_loader('../input/chest-xray-pneumonia/chest_xray/test')
train = data_loader('../input/chest-xray-pneumonia/chest_xray/train')
def normalize_list(train):
for pair in train:
pair[0] = np.array(pair[0]) / 255
return train
train = normalize_list(train)
test = normalize_list(test)
val = normalize_list(val)
def make_x_y(data):
X = []
Y = []
for pair in data:
X.append(pair[0])
Y.append(pair[1])
return (np.array(X), np.array(Y))
x_train, y_train = make_x_y(train)
x_test, y_test = make_x_y(test)
x_val, y_val = make_x_y(val)
x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
cuml_model = cuRFC(max_features=1.0, n_bins=8, n_estimators=40)
cuml_model.fit(x_train.astype('float32'), y_train.astype('float32'))
y_rfc_predict = cuml_model.predict(x_test.astype('float32'))
y_test_temp = y_test.reshape(1, len(y_test))[0]
y_np_test = np.asnumpy(y_test_temp)
y_np_rfc_predict = np.asnumpy(y_rfc_predict)
print(type(y_np_test))
print(type(y_np_rfc_predict)) | code |
90108999/cell_27 | [
"text_plain_output_1.png"
] | from cuml.metrics.confusion_matrix import confusion_matrix
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import cupy as np
import cv2
import matplotlib.pyplot as plt
import os
import seaborn as sns
image_size = 200
labels = ['PNEUMONIA', 'NORMAL']
def data_loader(data_dir):
data = list()
for label in labels:
path = os.path.join(data_dir, label)
class_num = labels.index(label)
for img in os.listdir(path):
img_arr = cv2.imread(os.path.join(path, img), 0)
resized_arr = cv2.resize(img_arr, (image_size, image_size))
data.append([resized_arr, class_num])
return data
val = data_loader('../input/chest-xray-pneumonia/chest_xray/val')
test = data_loader('../input/chest-xray-pneumonia/chest_xray/test')
train = data_loader('../input/chest-xray-pneumonia/chest_xray/train')
def normalize_list(train):
for pair in train:
pair[0] = np.array(pair[0]) / 255
return train
train = normalize_list(train)
test = normalize_list(test)
val = normalize_list(val)
def make_x_y(data):
X = []
Y = []
for pair in data:
X.append(pair[0])
Y.append(pair[1])
return (np.array(X), np.array(Y))
x_train, y_train = make_x_y(train)
x_test, y_test = make_x_y(test)
x_val, y_val = make_x_y(val)
x_train = x_train.reshape(len(x_train), 200 * 200)
x_test = x_test.reshape(len(x_test), 200 * 200)
x_val = x_val.reshape(len(x_val), 200 * 200)
cuml_model = cuRFC(max_features=1.0, n_bins=8, n_estimators=40)
cuml_model.fit(x_train.astype('float32'), y_train.astype('float32'))
y_rfc_predict = cuml_model.predict(x_test.astype('float32'))
y_test_temp = y_test.reshape(1, len(y_test))[0]
y_np_test = np.asnumpy(y_test_temp)
y_np_rfc_predict = np.asnumpy(y_rfc_predict)
print('confusion matrix of model: \n')
cmap = confusion_matrix(y_np_test, y_np_rfc_predict)
plt.figure(figsize=(4, 4), dpi=150)
hm = sns.heatmap(data=cmap, annot=True, fmt='g') | code |
90108999/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
print(x_val.shape)
print(y_val.shape) | code |
18146033/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.values[train_csv.sirna == i] for i in range(10)]).transpose()
plate_groups = np.zeros((1108, 4), int)
for sirna in range(1108):
grp = train_csv.loc[train_csv.sirna == sirna, :].plate.value_counts().index.values
assert len(grp) == 3
plate_groups[sirna, 0:3] = grp
plate_groups[sirna, 3] = 10 - grp.sum()
plate_groups[:10, :]
all_test_exp = test_csv.experiment.unique()
group_plate_probs = np.zeros((len(all_test_exp), 4))
for idx in range(len(all_test_exp)):
print('Experiment', idx)
preds = sub.loc[test_csv.experiment == all_test_exp[idx], 'sirna'].values
pp_mult = np.zeros((len(preds), 1108))
pp_mult[range(len(preds)), preds] = 1
sub_test = test_csv.loc[test_csv.experiment == all_test_exp[idx], :]
assert len(pp_mult) == len(sub_test)
for j in range(4):
mask = np.repeat(plate_groups[np.newaxis, :, j], len(pp_mult), axis=0) == np.repeat(sub_test.plate.values[:, np.newaxis], 1108, axis=1)
group_plate_probs[idx, j] = np.array(pp_mult)[mask].sum() / len(pp_mult) | code |
18146033/cell_20 | [
"text_plain_output_1.png"
] | from keras.applications.densenet import DenseNet121
from keras.layers import (Activation, Dropout, Flatten, Dense, Input, Conv2D, GlobalAveragePooling2D)
from keras.models import Model
from tqdm import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import PIL
import cv2
from PIL import Image, ImageOps
from keras.models import Sequential, load_model
from keras.layers import Activation, Dropout, Flatten, Dense, Input, Conv2D, GlobalAveragePooling2D
from keras.applications.densenet import DenseNet121
import keras
from keras.models import Model
SIZE = 224
NUM_CLASSES = 1108
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.values[train_csv.sirna == i] for i in range(10)]).transpose()
plate_groups = np.zeros((1108, 4), int)
for sirna in range(1108):
grp = train_csv.loc[train_csv.sirna == sirna, :].plate.value_counts().index.values
assert len(grp) == 3
plate_groups[sirna, 0:3] = grp
plate_groups[sirna, 3] = 10 - grp.sum()
plate_groups[:10, :]
all_test_exp = test_csv.experiment.unique()
group_plate_probs = np.zeros((len(all_test_exp), 4))
for idx in range(len(all_test_exp)):
preds = sub.loc[test_csv.experiment == all_test_exp[idx], 'sirna'].values
pp_mult = np.zeros((len(preds), 1108))
pp_mult[range(len(preds)), preds] = 1
sub_test = test_csv.loc[test_csv.experiment == all_test_exp[idx], :]
assert len(pp_mult) == len(sub_test)
for j in range(4):
mask = np.repeat(plate_groups[np.newaxis, :, j], len(pp_mult), axis=0) == np.repeat(sub_test.plate.values[:, np.newaxis], 1108, axis=1)
group_plate_probs[idx, j] = np.array(pp_mult)[mask].sum() / len(pp_mult)
pd.DataFrame(group_plate_probs, index=all_test_exp)
exp_to_group = group_plate_probs.argmax(1)
def create_model(input_shape, n_out):
input_tensor = Input(shape=input_shape)
base_model = DenseNet121(include_top=False, weights=None, input_tensor=input_tensor)
x = GlobalAveragePooling2D()(base_model.output)
x = Dense(1024, activation='relu')(x)
final_output = Dense(n_out, activation='softmax', name='final_output')(x)
model = Model(input_tensor, final_output)
return model
model = create_model(input_shape=(SIZE, SIZE, 3), n_out=NUM_CLASSES)
model.load_weights('../input/recursion-cellular-keras-densenet/Densenet121.h5')
predicted = []
for i, name in tqdm(enumerate(test_csv['id_code'])):
path1 = os.path.join('../input/recursion-cellular-image-classification-224-jpg/test/test/', name + '_s1.jpeg')
image1 = cv2.imread(path1)
score_predict1 = model.predict(image1[np.newaxis] / 255)
path2 = os.path.join('../input/recursion-cellular-image-classification-224-jpg/test/test/', name + '_s2.jpeg')
image2 = cv2.imread(path2)
score_predict2 = model.predict(image2[np.newaxis] / 255)
predicted.append(0.5 * (score_predict1 + score_predict2))
predicted = np.stack(predicted).squeeze()
def select_plate_group(pp_mult, idx):
sub_test = test_csv.loc[test_csv.experiment == all_test_exp[idx], :]
assert len(pp_mult) == len(sub_test)
mask = np.repeat(plate_groups[np.newaxis, :, exp_to_group[idx]], len(pp_mult), axis=0) != np.repeat(sub_test.plate.values[:, np.newaxis], 1108, axis=1)
pp_mult[mask] = 0
return pp_mult
for idx in range(len(all_test_exp)):
indices = test_csv.experiment == all_test_exp[idx]
preds = predicted[indices, :].copy()
preds = select_plate_group(preds, idx)
sub.loc[indices, 'sirna'] = preds.argmax(1)
(sub.sirna == pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv').sirna).mean() | code |
18146033/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.values[train_csv.sirna == i] for i in range(10)]).transpose()
plate_groups = np.zeros((1108, 4), int)
for sirna in range(1108):
grp = train_csv.loc[train_csv.sirna == sirna, :].plate.value_counts().index.values
assert len(grp) == 3
plate_groups[sirna, 0:3] = grp
plate_groups[sirna, 3] = 10 - grp.sum()
plate_groups[:10, :] | code |
18146033/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import PIL
import cv2
from PIL import Image, ImageOps
from keras.models import Sequential, load_model
from keras.layers import Activation, Dropout, Flatten, Dense, Input, Conv2D, GlobalAveragePooling2D
from keras.applications.densenet import DenseNet121
import keras
from keras.models import Model
SIZE = 224
NUM_CLASSES = 1108 | code |
18146033/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.values[train_csv.sirna == i] for i in range(10)]).transpose()
plate_groups = np.zeros((1108, 4), int)
for sirna in range(1108):
grp = train_csv.loc[train_csv.sirna == sirna, :].plate.value_counts().index.values
assert len(grp) == 3
plate_groups[sirna, 0:3] = grp
plate_groups[sirna, 3] = 10 - grp.sum()
plate_groups[:10, :]
all_test_exp = test_csv.experiment.unique()
group_plate_probs = np.zeros((len(all_test_exp), 4))
for idx in range(len(all_test_exp)):
preds = sub.loc[test_csv.experiment == all_test_exp[idx], 'sirna'].values
pp_mult = np.zeros((len(preds), 1108))
pp_mult[range(len(preds)), preds] = 1
sub_test = test_csv.loc[test_csv.experiment == all_test_exp[idx], :]
assert len(pp_mult) == len(sub_test)
for j in range(4):
mask = np.repeat(plate_groups[np.newaxis, :, j], len(pp_mult), axis=0) == np.repeat(sub_test.plate.values[:, np.newaxis], 1108, axis=1)
group_plate_probs[idx, j] = np.array(pp_mult)[mask].sum() / len(pp_mult)
exp_to_group = group_plate_probs.argmax(1)
print(exp_to_group) | code |
18146033/cell_19 | [
"text_plain_output_1.png"
] | from keras.applications.densenet import DenseNet121
from keras.layers import (Activation, Dropout, Flatten, Dense, Input, Conv2D, GlobalAveragePooling2D)
from keras.models import Model
from tqdm import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import PIL
import cv2
from PIL import Image, ImageOps
from keras.models import Sequential, load_model
from keras.layers import Activation, Dropout, Flatten, Dense, Input, Conv2D, GlobalAveragePooling2D
from keras.applications.densenet import DenseNet121
import keras
from keras.models import Model
SIZE = 224
NUM_CLASSES = 1108
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.values[train_csv.sirna == i] for i in range(10)]).transpose()
plate_groups = np.zeros((1108, 4), int)
for sirna in range(1108):
grp = train_csv.loc[train_csv.sirna == sirna, :].plate.value_counts().index.values
assert len(grp) == 3
plate_groups[sirna, 0:3] = grp
plate_groups[sirna, 3] = 10 - grp.sum()
plate_groups[:10, :]
all_test_exp = test_csv.experiment.unique()
group_plate_probs = np.zeros((len(all_test_exp), 4))
for idx in range(len(all_test_exp)):
preds = sub.loc[test_csv.experiment == all_test_exp[idx], 'sirna'].values
pp_mult = np.zeros((len(preds), 1108))
pp_mult[range(len(preds)), preds] = 1
sub_test = test_csv.loc[test_csv.experiment == all_test_exp[idx], :]
assert len(pp_mult) == len(sub_test)
for j in range(4):
mask = np.repeat(plate_groups[np.newaxis, :, j], len(pp_mult), axis=0) == np.repeat(sub_test.plate.values[:, np.newaxis], 1108, axis=1)
group_plate_probs[idx, j] = np.array(pp_mult)[mask].sum() / len(pp_mult)
exp_to_group = group_plate_probs.argmax(1)
def create_model(input_shape, n_out):
input_tensor = Input(shape=input_shape)
base_model = DenseNet121(include_top=False, weights=None, input_tensor=input_tensor)
x = GlobalAveragePooling2D()(base_model.output)
x = Dense(1024, activation='relu')(x)
final_output = Dense(n_out, activation='softmax', name='final_output')(x)
model = Model(input_tensor, final_output)
return model
model = create_model(input_shape=(SIZE, SIZE, 3), n_out=NUM_CLASSES)
model.load_weights('../input/recursion-cellular-keras-densenet/Densenet121.h5')
predicted = []
for i, name in tqdm(enumerate(test_csv['id_code'])):
path1 = os.path.join('../input/recursion-cellular-image-classification-224-jpg/test/test/', name + '_s1.jpeg')
image1 = cv2.imread(path1)
score_predict1 = model.predict(image1[np.newaxis] / 255)
path2 = os.path.join('../input/recursion-cellular-image-classification-224-jpg/test/test/', name + '_s2.jpeg')
image2 = cv2.imread(path2)
score_predict2 = model.predict(image2[np.newaxis] / 255)
predicted.append(0.5 * (score_predict1 + score_predict2))
predicted = np.stack(predicted).squeeze()
def select_plate_group(pp_mult, idx):
sub_test = test_csv.loc[test_csv.experiment == all_test_exp[idx], :]
assert len(pp_mult) == len(sub_test)
mask = np.repeat(plate_groups[np.newaxis, :, exp_to_group[idx]], len(pp_mult), axis=0) != np.repeat(sub_test.plate.values[:, np.newaxis], 1108, axis=1)
pp_mult[mask] = 0
return pp_mult
for idx in range(len(all_test_exp)):
print('Experiment', idx)
indices = test_csv.experiment == all_test_exp[idx]
preds = predicted[indices, :].copy()
preds = select_plate_group(preds, idx)
sub.loc[indices, 'sirna'] = preds.argmax(1) | code |
18146033/cell_16 | [
"text_plain_output_1.png"
] | from keras.applications.densenet import DenseNet121
from keras.layers import (Activation, Dropout, Flatten, Dense, Input, Conv2D, GlobalAveragePooling2D)
from keras.models import Model
from tqdm import tqdm
import cv2
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import PIL
import cv2
from PIL import Image, ImageOps
from keras.models import Sequential, load_model
from keras.layers import Activation, Dropout, Flatten, Dense, Input, Conv2D, GlobalAveragePooling2D
from keras.applications.densenet import DenseNet121
import keras
from keras.models import Model
SIZE = 224
NUM_CLASSES = 1108
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.values[train_csv.sirna == i] for i in range(10)]).transpose()
plate_groups = np.zeros((1108, 4), int)
for sirna in range(1108):
grp = train_csv.loc[train_csv.sirna == sirna, :].plate.value_counts().index.values
assert len(grp) == 3
plate_groups[sirna, 0:3] = grp
plate_groups[sirna, 3] = 10 - grp.sum()
plate_groups[:10, :]
all_test_exp = test_csv.experiment.unique()
group_plate_probs = np.zeros((len(all_test_exp), 4))
for idx in range(len(all_test_exp)):
preds = sub.loc[test_csv.experiment == all_test_exp[idx], 'sirna'].values
pp_mult = np.zeros((len(preds), 1108))
pp_mult[range(len(preds)), preds] = 1
sub_test = test_csv.loc[test_csv.experiment == all_test_exp[idx], :]
assert len(pp_mult) == len(sub_test)
for j in range(4):
mask = np.repeat(plate_groups[np.newaxis, :, j], len(pp_mult), axis=0) == np.repeat(sub_test.plate.values[:, np.newaxis], 1108, axis=1)
group_plate_probs[idx, j] = np.array(pp_mult)[mask].sum() / len(pp_mult)
def create_model(input_shape, n_out):
input_tensor = Input(shape=input_shape)
base_model = DenseNet121(include_top=False, weights=None, input_tensor=input_tensor)
x = GlobalAveragePooling2D()(base_model.output)
x = Dense(1024, activation='relu')(x)
final_output = Dense(n_out, activation='softmax', name='final_output')(x)
model = Model(input_tensor, final_output)
return model
model = create_model(input_shape=(SIZE, SIZE, 3), n_out=NUM_CLASSES)
model.load_weights('../input/recursion-cellular-keras-densenet/Densenet121.h5')
predicted = []
for i, name in tqdm(enumerate(test_csv['id_code'])):
path1 = os.path.join('../input/recursion-cellular-image-classification-224-jpg/test/test/', name + '_s1.jpeg')
image1 = cv2.imread(path1)
score_predict1 = model.predict(image1[np.newaxis] / 255)
path2 = os.path.join('../input/recursion-cellular-image-classification-224-jpg/test/test/', name + '_s2.jpeg')
image2 = cv2.imread(path2)
score_predict2 = model.predict(image2[np.newaxis] / 255)
predicted.append(0.5 * (score_predict1 + score_predict2)) | code |
18146033/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.values[train_csv.sirna == i] for i in range(10)]).transpose()
plate_groups = np.zeros((1108, 4), int)
for sirna in range(1108):
grp = train_csv.loc[train_csv.sirna == sirna, :].plate.value_counts().index.values
assert len(grp) == 3
plate_groups[sirna, 0:3] = grp
plate_groups[sirna, 3] = 10 - grp.sum()
plate_groups[:10, :]
all_test_exp = test_csv.experiment.unique()
group_plate_probs = np.zeros((len(all_test_exp), 4))
for idx in range(len(all_test_exp)):
preds = sub.loc[test_csv.experiment == all_test_exp[idx], 'sirna'].values
pp_mult = np.zeros((len(preds), 1108))
pp_mult[range(len(preds)), preds] = 1
sub_test = test_csv.loc[test_csv.experiment == all_test_exp[idx], :]
assert len(pp_mult) == len(sub_test)
for j in range(4):
mask = np.repeat(plate_groups[np.newaxis, :, j], len(pp_mult), axis=0) == np.repeat(sub_test.plate.values[:, np.newaxis], 1108, axis=1)
group_plate_probs[idx, j] = np.array(pp_mult)[mask].sum() / len(pp_mult)
pd.DataFrame(group_plate_probs, index=all_test_exp) | code |
18146033/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
train_csv = pd.read_csv('../input/recursion-cellular-image-classification/train.csv')
test_csv = pd.read_csv('../input/recursion-cellular-image-classification/test.csv')
sub = pd.read_csv('../input/recursion-cellular-keras-densenet/submission.csv')
np.stack([train_csv.plate.values[train_csv.sirna == i] for i in range(10)]).transpose() | code |
72062529/cell_7 | [
"text_plain_output_1.png"
] | # @title Install dependencies
# @markdown Download dataset, modules, and files needed for the tutorial from GitHub.
# @markdown Download from OSF. Original repo: https://github.com/colleenjg/neuromatch_ssl_tutorial.git
import os, sys, importlib
REPO_PATH = "neuromatch_ssl_tutorial"
download_str = "Downloading"
if os.path.exists(REPO_PATH):
download_str = "Redownloading"
!rm -rf $REPO_PATH
# download from github repo directly
#!git clone git://github.com/colleenjg/neuromatch_ssl_tutorial.git --quiet
from io import BytesIO
from urllib.request import urlopen
from zipfile import ZipFile
zipurl = 'https://osf.io/smqvg/download'
print(f"{download_str} and unzipping the file... Please wait.")
with urlopen(zipurl) as zipresp:
with ZipFile(BytesIO(zipresp.read())) as zfile:
zfile.extractall()
print("Download completed!")
# @markdown Import modules designed for use in this tutorials
from neuromatch_ssl_tutorial.modules import data, load, models, plot_util
from neuromatch_ssl_tutorial.modules import data, load, models, plot_util
importlib.reload(data);
importlib.reload(load);
importlib.reload(models)
importlib.reload(plot_util);
!pip install git+https://github.com/NeuromatchAcademy/evaltools --quiet
from evaltools.airtable import AirtableForm
# generate airtable form
atform = AirtableForm('appn7VdPRseSoMXEG','W3D1_T1','https://portal.neuromatchacademy.org/api/redirect/to/47de6f8f-1265-4a74-88c4-7dfa6e64b35a') | code |
72062529/cell_17 | [
"text_plain_output_1.png"
] | from IPython.display import IFrame
from IPython.display import IFrame
from IPython.display import YouTubeVideo
from IPython.display import display, Image # to visualize images
from ipywidgets import widgets
import ipywidgets as widgets # interactive display
from ipywidgets import widgets
out2 = widgets.Output()
with out2:
from IPython.display import IFrame
class BiliVideo(IFrame):
def __init__(self, id, page=1, width=400, height=300, **kwargs):
self.id = id
src = 'https://player.bilibili.com/player.html?bvid={0}&page={1}'.format(id, page)
super(BiliVideo, self).__init__(src, width, height, **kwargs)
video = BiliVideo(id=f'BV1D64y1s78e', width=854, height=480, fs=1)
print('Video available at https://www.bilibili.com/video/{0}'.format(video.id))
display(video)
out1 = widgets.Output()
with out1:
from IPython.display import YouTubeVideo
video = YouTubeVideo(id=f'Q3b_EqFUI00', width=854, height=480, fs=1, rel=0)
print('Video available at https://youtube.com/watch?v=' + video.id)
display(video)
out = widgets.Tab([out1, out2])
out.set_title(0, 'Youtube')
out.set_title(1, 'Bilibili')
atform.add_event('Video 0: Introduction')
display(out) | code |
72062529/cell_14 | [
"text_html_output_1.png"
] | from neuromatch_ssl_tutorial.modules import data, load, models, plot_util
from neuromatch_ssl_tutorial.modules import data, load, models, plot_util
import matplotlib.pyplot as plt
import numpy as np
import random
import torch
import torch
# @title Plotting functions
# @markdown Function to plot a histogram of RSM values: `plot_rsm_histogram(rsms, colors)`
def plot_rsm_histogram(rsms, colors, labels=None, nbins=100):
fig, ax = plt.subplots(1)
ax.set_title("Histogram of RSM values", y=1.05)
min_val = np.min([np.nanmin(rsm) for rsm in rsms])
max_val = np.max([np.nanmax(rsm) for rsm in rsms])
bins = np.linspace(min_val, max_val, nbins+1)
if labels is None:
labels = [labels] * len(rsms)
elif len(labels) != len(rsms):
raise ValueError("If providing labels, must provide as many as RSMs.")
if len(rsms) != len(colors):
raise ValueError("Must provide as may colors as RSMs.")
for r, rsm in enumerate(rsms):
ax.hist(
rsm.reshape(-1), bins, density=True, alpha=0.4,
color=colors[r], label=labels[r]
)
ax.axvline(x=0, ls="dashed", alpha=0.6, color="k")
ax.set_ylabel("Density")
ax.set_xlabel("Similarity values")
ax.legend()
plt.show()
from IPython.display import display, Image
def test_custom_torch_RSM_fct(custom_torch_RSM_fct):
rand_feats = torch.rand(100, 1000)
RSM_custom = custom_torch_RSM_fct(rand_feats)
RSM_ground_truth = data.calculate_torch_RSM(rand_feats)
def test_custom_contrastive_loss_fct(custom_simclr_contrastive_loss):
rand_proj_feat1 = torch.rand(100, 1000)
rand_proj_feat2 = torch.rand(100, 1000)
loss_custom = custom_simclr_contrastive_loss(rand_proj_feat1, rand_proj_feat2)
loss_ground_truth = models.contrastive_loss(rand_proj_feat1, rand_proj_feat2)
import random
import torch
def set_seed(seed=None, seed_torch=True):
if seed is None:
seed = np.random.choice(2 ** 32)
random.seed(seed)
np.random.seed(seed)
if seed_torch:
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def seed_worker(worker_id):
worker_seed = torch.initial_seed() % 2 ** 32
np.random.seed(worker_seed)
random.seed(worker_seed)
def set_device():
device = 'cuda' if torch.cuda.is_available() else 'cpu'
return device
SEED = 2021
set_seed(seed=SEED)
DEVICE = set_device() | code |
72062529/cell_5 | [
"text_plain_output_1.png"
] | from IPython.display import IFrame
from IPython.display import IFrame
IFrame(src=f'https://mfr.ca-1.osf.io/render?url=https://osf.io/wvt34/?direct%26mode=render%26action=download%26mode=render', width=854, height=480) | code |
16112056/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.duplicated().sum()
health_df1.drop_duplicates(inplace=True)
health_df1.duplicated().sum()
health_df1 = health_df1.drop(['Indicator', 'Place', 'BCHC Requested Methodology', 'Source', 'Methods', 'Notes'], axis=1)
plt.figure(figsize=(15, 5))
ax = sns.barplot(x='Indicator Category', y='Value', data=health_df1)
ax.set_title('Indicator Category vs Value')
plt.xlabel('Indicator Category')
plt.ylabel('Value')
plt.show(ax) | code |
16112056/cell_4 | [
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.duplicated().sum()
health_df1.drop_duplicates(inplace=True)
health_df1.duplicated().sum()
health_df1.info() | code |
16112056/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.duplicated().sum() | code |
16112056/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.duplicated().sum()
health_df1.drop_duplicates(inplace=True)
health_df1.duplicated().sum()
health_df1 = health_df1.drop(['Indicator', 'Place', 'BCHC Requested Methodology', 'Source', 'Methods', 'Notes'], axis=1)
plt.figure(figsize=(15,5))
ax = sns.barplot(x='Indicator Category',y='Value', data=health_df1)
ax.set_title('Indicator Category vs Value')
plt.xlabel("Indicator Category")
plt.ylabel('Value')
plt.show(ax)
plt.figure(figsize=(15,5))
ax = sns.barplot(x='Indicator Category',y='Value', hue='Gender',data=health_df1)
ax.set_title('Indicator Category vs Value by Gender')
plt.xlabel("Indicator Category")
plt.ylabel('Value')
plt.show(ax)
plt.figure(figsize=(15, 5))
ax = sns.barplot(x='State', y='Value', hue='Gender', data=health_df1)
ax.set_title('State vs Value by Gender')
plt.xlabel('State')
plt.ylabel('Value')
plt.show(ax) | code |
16112056/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input'))
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.head(3) | code |
16112056/cell_8 | [
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.duplicated().sum()
health_df1.drop_duplicates(inplace=True)
health_df1.duplicated().sum()
health_df1 = health_df1.drop(['Indicator', 'Place', 'BCHC Requested Methodology', 'Source', 'Methods', 'Notes'], axis=1)
health_df1.head() | code |
16112056/cell_3 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.duplicated().sum()
health_df1.drop_duplicates(inplace=True)
health_df1.duplicated().sum() | code |
16112056/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
health_df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
health_df1 = health_df.copy()
health_df1.duplicated().sum()
health_df1.drop_duplicates(inplace=True)
health_df1.duplicated().sum()
health_df1 = health_df1.drop(['Indicator', 'Place', 'BCHC Requested Methodology', 'Source', 'Methods', 'Notes'], axis=1)
plt.figure(figsize=(15,5))
ax = sns.barplot(x='Indicator Category',y='Value', data=health_df1)
ax.set_title('Indicator Category vs Value')
plt.xlabel("Indicator Category")
plt.ylabel('Value')
plt.show(ax)
plt.figure(figsize=(15, 5))
ax = sns.barplot(x='Indicator Category', y='Value', hue='Gender', data=health_df1)
ax.set_title('Indicator Category vs Value by Gender')
plt.xlabel('Indicator Category')
plt.ylabel('Value')
plt.show(ax) | code |
128011806/cell_4 | [
"text_plain_output_1.png"
] | model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary() | code |
128011806/cell_34 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
import tensorflow as tf
model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary()
class_names = ['happy', 'sad']
data = np.ndarray(shape=(48, 48, 1), dtype=np.float32)
image = Image.open('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/dataset/sad/PrivateTest_568359.jpg').convert('RGB')
gray_image = image.convert('L')
size = (48, 48)
image = ImageOps.fit(gray_image, size, Image.Resampling.LANCZOS)
image_array = np.asarray(image)
image_array.shape
normalized_image_array = image_array.astype(np.float32) / 127.5 - 1
data_tensor = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor = tf.expand_dims(data_tensor, axis=0)
data_tensor.shape
prediction = model.predict(data_tensor)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]
image2 = Image.open('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/dataset/happy/PrivateTest_61167984.jpg').convert('RGB')
gray_image = image2.convert('L')
size = (48, 48)
image2 = ImageOps.fit(gray_image, size, Image.Resampling.LANCZOS)
image_array2 = np.asarray(image2)
image_array2.shape
normalized_image_array2 = image_array2.astype(np.float32) / 127.5 - 1
data_tensor2 = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor2 = tf.expand_dims(data_tensor2, axis=0)
data_tensor2.shape
prediction = model.predict(data_tensor2)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]
print('Class:', class_name, end=' ')
print('Confidence Score:', confidence_score) | code |
128011806/cell_33 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
import tensorflow as tf
model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary()
class_names = ['happy', 'sad']
data = np.ndarray(shape=(48, 48, 1), dtype=np.float32)
image = Image.open('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/dataset/sad/PrivateTest_568359.jpg').convert('RGB')
gray_image = image.convert('L')
size = (48, 48)
image = ImageOps.fit(gray_image, size, Image.Resampling.LANCZOS)
image_array = np.asarray(image)
image_array.shape
normalized_image_array = image_array.astype(np.float32) / 127.5 - 1
data_tensor = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor = tf.expand_dims(data_tensor, axis=0)
data_tensor.shape
prediction = model.predict(data_tensor)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]
image2 = Image.open('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/dataset/happy/PrivateTest_61167984.jpg').convert('RGB')
gray_image = image2.convert('L')
size = (48, 48)
image2 = ImageOps.fit(gray_image, size, Image.Resampling.LANCZOS)
image_array2 = np.asarray(image2)
image_array2.shape
normalized_image_array2 = image_array2.astype(np.float32) / 127.5 - 1
data_tensor2 = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor2 = tf.expand_dims(data_tensor2, axis=0)
data_tensor2.shape
prediction = model.predict(data_tensor2)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index] | code |
128011806/cell_19 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
import tensorflow as tf
model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary()
class_names = ['happy', 'sad']
data = np.ndarray(shape=(48, 48, 1), dtype=np.float32)
image = Image.open('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/dataset/sad/PrivateTest_568359.jpg').convert('RGB')
gray_image = image.convert('L')
size = (48, 48)
image = ImageOps.fit(gray_image, size, Image.Resampling.LANCZOS)
image_array = np.asarray(image)
image_array.shape
normalized_image_array = image_array.astype(np.float32) / 127.5 - 1
data_tensor = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor = tf.expand_dims(data_tensor, axis=0)
data_tensor.shape
prediction = model.predict(data_tensor)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]
print('Class:', class_name, end=' ')
print('Confidence Score:', confidence_score) | code |
128011806/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np
import tensorflow as tf | code |
128011806/cell_18 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
import tensorflow as tf
model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary()
class_names = ['happy', 'sad']
data = np.ndarray(shape=(48, 48, 1), dtype=np.float32)
image = Image.open('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/dataset/sad/PrivateTest_568359.jpg').convert('RGB')
gray_image = image.convert('L')
size = (48, 48)
image = ImageOps.fit(gray_image, size, Image.Resampling.LANCZOS)
image_array = np.asarray(image)
image_array.shape
normalized_image_array = image_array.astype(np.float32) / 127.5 - 1
data_tensor = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor = tf.expand_dims(data_tensor, axis=0)
data_tensor.shape
prediction = model.predict(data_tensor)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index] | code |
128011806/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np
import tensorflow as tf
data = np.ndarray(shape=(48, 48, 1), dtype=np.float32)
data_tensor = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor = tf.expand_dims(data_tensor, axis=0)
data_tensor2 = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor2 = tf.expand_dims(data_tensor2, axis=0)
data_tensor2.shape | code |
128011806/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import tensorflow as tf
data = np.ndarray(shape=(48, 48, 1), dtype=np.float32)
data_tensor = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor = tf.expand_dims(data_tensor, axis=0)
data_tensor.shape | code |
128011806/cell_27 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
import tensorflow as tf
model = load_model('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/model_3.h5', compile=False)
model.summary()
class_names = ['happy', 'sad']
data = np.ndarray(shape=(48, 48, 1), dtype=np.float32)
image = Image.open('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/dataset/sad/PrivateTest_568359.jpg').convert('RGB')
gray_image = image.convert('L')
size = (48, 48)
image = ImageOps.fit(gray_image, size, Image.Resampling.LANCZOS)
image_array = np.asarray(image)
image_array.shape
normalized_image_array = image_array.astype(np.float32) / 127.5 - 1
data_tensor = tf.convert_to_tensor(data, dtype=tf.float32)
data_tensor = tf.expand_dims(data_tensor, axis=0)
data_tensor.shape
prediction = model.predict(data_tensor)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]
image2 = Image.open('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/dataset/happy/PrivateTest_61167984.jpg').convert('RGB')
gray_image = image2.convert('L')
size = (48, 48)
image2 = ImageOps.fit(gray_image, size, Image.Resampling.LANCZOS)
image_array2 = np.asarray(image2)
image_array2.shape | code |
128011806/cell_12 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
data = np.ndarray(shape=(48, 48, 1), dtype=np.float32)
image = Image.open('/kaggle/input/ahsan-model-3-testing-implementation/from_kaggle/dataset/sad/PrivateTest_568359.jpg').convert('RGB')
gray_image = image.convert('L')
size = (48, 48)
image = ImageOps.fit(gray_image, size, Image.Resampling.LANCZOS)
image_array = np.asarray(image)
image_array.shape | code |
122253838/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
url = 'https://data.boston.gov/api/3/action/datastore_search?resource_id=c13199bf-49a1-488d-b8e9-55e49523ef81&limit=90000'
js = pd.read_json(url)
df = pd.DataFrame(js['result']['records'])
df = df.set_index('timestamp')
df.columns = df.columns.str.lower()
df = df.drop(['_id'], axis=1)
df.index = df.index.astype('datetime64[ns]')
df.index = df.index - pd.tseries.offsets.Day()
df.index = pd.to_datetime(df.index.date)
df['usage'] = df['usage'].astype('int32')
df.info() | code |
73083713/cell_21 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum()
customer_data_cleaned = customer_data.dropna()
from datetime import date
def get_age(birthyear):
return date.today().year - birthyear
ages = customer_data_cleaned.Year_Birth.map(get_age)
customer_data_cleaned['Age'] = customer_data_cleaned.Year_Birth.map(get_age)
customer_data_cleaned.sort_values(by='Year_Birth').head()
customer_data_cleaned.drop([11004, 1150, 7829], inplace=True)
customer_data_cleaned['Spending'] = customer_data_cleaned.MntWines + customer_data_cleaned.MntFruits + customer_data_cleaned.MntMeatProducts + customer_data_cleaned.MntFishProducts + customer_data_cleaned.MntSweetProducts + customer_data_cleaned.MntGoldProds
customer_data_cleaned['Time_With_Company'] = pd.to_datetime(customer_data_cleaned.Dt_Customer, dayfirst=True, format='%d-%m-%Y')
customer_data_cleaned['Time_With_Company'] = pd.to_numeric(customer_data_cleaned.Time_With_Company.dt.date.apply(lambda z: date.today() - z).dt.days, downcast='integer') / 30
customer_data_cleaned.Education.unique() | code |
73083713/cell_13 | [
"text_html_output_1.png"
] | from datetime import date
import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum()
customer_data_cleaned = customer_data.dropna()
from datetime import date
def get_age(birthyear):
return date.today().year - birthyear
ages = customer_data_cleaned.Year_Birth.map(get_age)
customer_data_cleaned['Age'] = customer_data_cleaned.Year_Birth.map(get_age)
customer_data_cleaned.Age.describe() | code |
73083713/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum() | code |
73083713/cell_23 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum()
customer_data_cleaned = customer_data.dropna()
from datetime import date
def get_age(birthyear):
return date.today().year - birthyear
ages = customer_data_cleaned.Year_Birth.map(get_age)
customer_data_cleaned['Age'] = customer_data_cleaned.Year_Birth.map(get_age)
customer_data_cleaned.sort_values(by='Year_Birth').head()
customer_data_cleaned.drop([11004, 1150, 7829], inplace=True)
customer_data_cleaned['Spending'] = customer_data_cleaned.MntWines + customer_data_cleaned.MntFruits + customer_data_cleaned.MntMeatProducts + customer_data_cleaned.MntFishProducts + customer_data_cleaned.MntSweetProducts + customer_data_cleaned.MntGoldProds
customer_data_cleaned['Time_With_Company'] = pd.to_datetime(customer_data_cleaned.Dt_Customer, dayfirst=True, format='%d-%m-%Y')
customer_data_cleaned['Time_With_Company'] = pd.to_numeric(customer_data_cleaned.Time_With_Company.dt.date.apply(lambda z: date.today() - z).dt.days, downcast='integer') / 30
customer_data_cleaned.Education.unique()
customer_data_cleaned.Marital_Status.unique() | code |
73083713/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.head() | code |
73083713/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape | code |
73083713/cell_32 | [
"text_html_output_1.png"
] | from datetime import date
import numpy as np
import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum()
customer_data_cleaned = customer_data.dropna()
from datetime import date
def get_age(birthyear):
return date.today().year - birthyear
ages = customer_data_cleaned.Year_Birth.map(get_age)
customer_data_cleaned['Age'] = customer_data_cleaned.Year_Birth.map(get_age)
customer_data_cleaned.sort_values(by='Year_Birth').head()
customer_data_cleaned.drop([11004, 1150, 7829], inplace=True)
customer_data_cleaned['Spending'] = customer_data_cleaned.MntWines + customer_data_cleaned.MntFruits + customer_data_cleaned.MntMeatProducts + customer_data_cleaned.MntFishProducts + customer_data_cleaned.MntSweetProducts + customer_data_cleaned.MntGoldProds
customer_data_cleaned['Time_With_Company'] = pd.to_datetime(customer_data_cleaned.Dt_Customer, dayfirst=True, format='%d-%m-%Y')
customer_data_cleaned['Time_With_Company'] = pd.to_numeric(customer_data_cleaned.Time_With_Company.dt.date.apply(lambda z: date.today() - z).dt.days, downcast='integer') / 30
customer_data_cleaned.Education.unique()
customer_data_cleaned.Marital_Status.unique()
customer_data_cleaned.Marital_Status = customer_data_cleaned.Marital_Status.replace({'Divorced': 'Single', 'Together': 'Partner', 'Married': 'Partner', 'Widow': 'Single', 'Alone': 'Single', 'Absurd': 'Single', 'YOLO': 'Single'})
customer_data_cleaned['Children'] = customer_data_cleaned.Kidhome + customer_data_cleaned.Teenhome
customer_data_cleaned['Has_Child'] = np.where(customer_data_cleaned.Children > 0, 'Has Child', 'No Child')
customer_data_cleaned = customer_data_cleaned.rename(columns={'MntWines': 'Wine', 'MntFruits': 'Fruit', 'MntMeatProducts': 'Meat', 'MntFishProducts': 'Fish', 'MntSweetProducts': 'Sweets', 'MntGoldProds': 'Gold'})
customer_data_cleaned = customer_data_cleaned.rename(columns={'NumWebPurchases': 'Web', 'NumCatalogPurchases': 'Catalog', 'NumStorePurchases': 'Store', 'NumWebVisitsMonth': 'WebVisits'})
customer_data_cleaned.Web.describe() | code |
73083713/cell_15 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
customer_data = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', sep='\t', index_col='ID')
customer_data.shape
customer_data.isnull().sum()
customer_data_cleaned = customer_data.dropna()
from datetime import date
def get_age(birthyear):
return date.today().year - birthyear
ages = customer_data_cleaned.Year_Birth.map(get_age)
customer_data_cleaned['Age'] = customer_data_cleaned.Year_Birth.map(get_age)
customer_data_cleaned.sort_values(by='Year_Birth').head() | code |
33104665/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull()))
plt.figure(figsize = (16,8))
#Let's verify the correlation of each value
ax = sb.heatmap(Ydata[['views', 'likes', 'dislikes', 'comment_count']].corr(), \
annot=True, annot_kws={"size": 20}, cmap=cm.coolwarm, linewidths=0.5, linecolor='black')
plt.yticks(rotation=30, fontsize=20)
plt.xticks(rotation=30, fontsize=20)
plt.title("\nCorrelation between views, likes, dislikes & comments\n", fontsize=25)
plt.show()
colors = ['#FF6600', '#FFCCCC']
labels = ('likes', 'dislikes')
plt.suptitle('Information on data_split', fontsize=20)
data['Ydata'].value_counts().plot.pie(autopct='%1.2f%%', shadow=True, colors=colors, labels=labels, fontsize=12, startangle=70) | code |
33104665/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull()))
Ydata['trending_date'].head() | code |
33104665/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull()))
plt.figure(figsize=(16, 8))
ax = sb.heatmap(Ydata[['views', 'likes', 'dislikes', 'comment_count']].corr(), annot=True, annot_kws={'size': 20}, cmap=cm.coolwarm, linewidths=0.5, linecolor='black')
plt.yticks(rotation=30, fontsize=20)
plt.xticks(rotation=30, fontsize=20)
plt.title('\nCorrelation between views, likes, dislikes & comments\n', fontsize=25)
plt.show() | code |
33104665/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 |
33104665/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.info() | code |
33104665/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull())) | code |
33104665/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull()))
column_list = ['views', 'likes', 'dislikes', 'comment_count']
corr_matrix = Ydata[column_list].corr()
corr_matrix | code |
33104665/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.apply(lambda x: sum(x.isnull()))
sns.countplot(x='likes', data=Ydata) | code |
33104665/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Ydata = pd.read_csv('../input/youtube-new/USvideos.csv')
original_data = Ydata.copy()
Ydata.head() | code |
50244797/cell_13 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_text.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
result = pd.merge(variant, text_data, on='ID', how='left')
result[result.isnull().any(axis=1)] | code |
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