kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
4,566,279 | high_corr_col = filter_correlation(train, 0.7)
high_corr_col<drop_column> | results = pd.Series(Y_pred, name="Label")
submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),results],axis = 1)
submission.to_csv("cnn_mnist_predictions.csv",index=False ) | Digit Recognizer |
3,437,991 | train = train.drop(['1stFlrSF', 'GarageArea', 'TotRmsAbvGrd'], axis = 1)
test = test.drop(['1stFlrSF', 'GarageArea', 'TotRmsAbvGrd'], axis = 1 )<drop_column> | df_train = pd.read_csv('.. /input/train.csv')
df_test = pd.read_csv('.. /input/test.csv' ) | Digit Recognizer |
3,437,991 | train.drop(['MiscVal', 'MoSold','YrSold'], axis = 1, inplace = True)
test.drop(['MiscVal', 'MoSold','YrSold'], axis = 1, inplace = True )<prepare_x_and_y> | y_train = df_train['label']
x_train = df_train.drop(labels = ['label'] , axis=1)
del df_train | Digit Recognizer |
3,437,991 | X = train.drop(['SalePrice'], axis = 1)
col_to_use = list(X.columns)
y = train['SalePrice']
print(X.shape)
print(y.shape )<define_variables> | y_train.value_counts() | Digit Recognizer |
3,437,991 | num_cols = [col for col in col_to_use if train[col].dtype in ['int64', 'float64']]
cat_cols = [col for col in col_to_use if train[col].dtype == 'object']
num_cols<choose_model_class> | x_train = x_train/255.0
df_test = df_test/255.0 | Digit Recognizer |
3,437,991 | num_processor = Pipeline(steps = [
('imputer', SimpleImputer(strategy='most_frequent')) ,
('scaler', MinMaxScaler())
] )<categorify> | y_train = to_categorical(y_train , num_classes = 10 ) | Digit Recognizer |
3,437,991 | cat_processor = Pipeline(steps = [
('imputer', SimpleImputer(strategy = 'most_frequent')) ,
('ohe', OneHotEncoder(handle_unknown = 'ignore', sparse = False))
] )<feature_engineering> | x_train , x_val , y_train , y_val = train_test_split(x_train , y_train , test_size =.1 , random_state = 0 ) | Digit Recognizer |
3,437,991 | preprocessor = ColumnTransformer([
('num', num_processor, num_cols),
('cat', cat_processor, cat_cols)
] )<split> | classifier = Sequential()
classifier.add(Conv2D(filters = 32, kernel_size =(5,5),padding = 'Same',
activation ='relu', input_shape =(28,28,1)))
classifier.add(Conv2D(filters = 32, kernel_size =(5,5),padding = 'Same',
activation ='relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Dropout(0.25))
classif... | Digit Recognizer |
3,437,991 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42 )<choose_model_class> | classifier.compile(optimizer = 'adam' , loss = "categorical_crossentropy", metrics=["accuracy"] ) | Digit Recognizer |
3,437,991 | model = LinearRegression()
model1 = Lasso()
model2 = Ridge()
model3 = DecisionTreeRegressor(max_leaf_nodes = 30, random_state = 42)
model4 = RandomForestRegressor(n_estimators = 500, random_state = 42)
model5 = XGBRegressor(n_estimators = 1000, learning_rate = 0.05, random_state = 42)
model6 = GradientBoostingRegres... | epochs = 30
batch_size = 126 | Digit Recognizer |
3,437,991 | def build_model(model):
clf = Pipeline(steps = [
('preprocessor', preprocessor),
('model', model)
])
clf.fit(X_train, y_train)
print(model)
print("Train set score:", clf.score(X_train, y_train))
print("Test set score:", clf.score(X_test, y_test))
print("
")
print("Train set rmse:", mean_squared_error(y_train, cl... | datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=10,
zoom_range = 0.1,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=False,
vertical_flip=False)
datagen.fit(x_t... | Digit Recognizer |
3,437,991 | predictions = build_model(model7 )<prepare_output> | history = classifier.fit_generator(datagen.flow(x_train,y_train, batch_size=batch_size),
epochs = epochs, validation_data =(x_val,y_val),
verbose = 2, steps_per_epoch=x_train.shape[0] // batch_size
) | Digit Recognizer |
3,437,991 | predictions = np.exp(predictions )<save_to_csv> | results = classifier.predict(df_test)
results = np.argmax(results,axis = 1)
results = pd.Series(results,name="Label" ) | Digit Recognizer |
3,437,991 | output = pd.DataFrame({'Id': Id, 'SalePrice': predictions})
output.to_csv('submission.csv', index = False)
sub = pd.read_csv('./submission.csv')
sub<import_modules> | submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),results],axis = 1)
submission.to_csv("cnn_mnist_datagen.csv",index=False ) | Digit Recognizer |
1,172,238 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from lightgbm import LGBMRegressor
from xgboost import XGBRegressor
import sklearn.metrics as metrics
import math<load_from_csv> | train_df = pd.read_csv('.. /input/train.csv')
test_df = pd.read_csv('.. /input/test.csv')
train_df.head(5 ) | Digit Recognizer |
1,172,238 | sample_submission = pd.read_csv(".. /input/house-prices-advanced-regression-techniques/sample_submission.csv")
test = pd.read_csv(".. /input/house-prices-advanced-regression-techniques/test.csv")
train = pd.read_csv(".. /input/house-prices-advanced-regression-techniques/train.csv")
c_test = test.copy()
c_train = tra... | train = train_df.values
test = test_df.values
trainX = train[:, 1:].reshape(train.shape[0], 28, 28, 1)
trainX = trainX.astype(float)
trainX /= 255.0 | Digit Recognizer |
1,172,238 | c_train['train'] = 1
c_test['train'] = 0
df = pd.concat([c_train, c_test], axis=0,sort=False )<create_dataframe> | trainY = kutils.to_categorical(train[:, 0])
class_num = trainY.shape[1]
print(class_num ) | Digit Recognizer |
1,172,238 | NAN = [(c, df[c].isna().mean() *100)for c in df]
NAN = pd.DataFrame(NAN, columns=["column_name", "percentage"] )<sort_values> | from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D | Digit Recognizer |
1,172,238 | NAN = NAN[NAN.percentage > 50]
NAN.sort_values("percentage", ascending=False )<drop_column> | model = Sequential()
model.add(Conv2D(16,(3, 3), padding='same', activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(32,(5, 5), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(1, 1)))
model.add(Conv2D(32,(3, 3), padding='same', activation='relu'))
model.add(Conv2D(64,(5... | Digit Recognizer |
1,172,238 | df = df.drop(['Alley','PoolQC','Fence','MiscFeature'],axis=1 )<count_missing_values> | model.fit(trainX, trainY, batch_size=64, epochs=100, verbose=2 ) | Digit Recognizer |
1,172,238 | null_counts = object_columns_df.isnull().sum()
print("Number of null values in each column:
{}".format(null_counts))<data_type_conversions> | testX = test.reshape(test.shape[0], 28, 28, 1)
testX = testX.astype(float)
testX /= 255.0
yPred = model.predict_classes(testX)
np.savetxt('mnist-cnn.csv', np.c_[range(1,len(yPred)+1),yPred], delimiter=',', header = 'ImageId,Label', comments = '', fmt='%d' ) | Digit Recognizer |
1,191,702 | columns_None = ['BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2','GarageType','GarageFinish','GarageQual','FireplaceQu','GarageCond']
object_columns_df[columns_None]= object_columns_df[columns_None].fillna('None' )<categorify> | df = pd.read_csv('.. /input/train.csv')
df.head() | Digit Recognizer |
1,191,702 | columns_with_lowNA = ['MSZoning','Utilities','Exterior1st','Exterior2nd','MasVnrType','Electrical','KitchenQual','Functional','SaleType']
object_columns_df[columns_with_lowNA] = object_columns_df[columns_with_lowNA].fillna(object_columns_df.mode().iloc[0] )<count_missing_values> | y = to_categorical(y ).astype("uint8")
print(y.shape ) | Digit Recognizer |
1,191,702 | null_counts = numerical_columns_df.isnull().sum()
print("Number of null values in each column:
{}".format(null_counts))<feature_engineering> | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
del df, X, y | Digit Recognizer |
1,191,702 | numerical_columns_df['GarageYrBlt'] = numerical_columns_df['GarageYrBlt'].fillna(numerical_columns_df['YrSold']-35)
numerical_columns_df['LotFrontage'] = numerical_columns_df['LotFrontage'].fillna(68 )<drop_column> | def create_model() :
model = Sequential()
model.add(Conv2D(32, 5, activation="relu", input_shape=(28, 28, 1)))
model.add(Conv2D(32, 5, activation="relu"))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.4))
model.add(Conv2D(64, 3, activation="relu", padding='same'))
model.add(Conv2D(64, 3, activation="relu... | Digit Recognizer |
1,191,702 | object_columns_df = object_columns_df.drop(['Heating','RoofMatl','Condition2','Street','Utilities'],axis=1 )<filter> | learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=2,
factor=0.4,
min_lr=3e-6)
early_stops = EarlyStopping(monitor='val_acc', min_delta=0, patience=6, verbose=2, mode='auto' ) | Digit Recognizer |
1,191,702 | Negatif = numerical_columns_df[numerical_columns_df['Age_House'] < 0]
Negatif<feature_engineering> | data_aug = ImageDataGenerator(rotation_range=20, width_shift_range=4, height_shift_range=4, zoom_range=0.1 ) | Digit Recognizer |
1,191,702 | numerical_columns_df['TotalBsmtBath'] = numerical_columns_df['BsmtFullBath'] + numerical_columns_df['BsmtFullBath']*0.5
numerical_columns_df['TotalBath'] = numerical_columns_df['FullBath'] + numerical_columns_df['HalfBath']*0.5
numerical_columns_df['TotalSA']=numerical_columns_df['TotalBsmtSF'] + numerical_columns_df['... | history = model.fit_generator(data_aug.flow(X_train, y_train, batch_size=128), steps_per_epoch=len(X_train)//128,
validation_data=(X_test, y_test), epochs=100, verbose=1, callbacks=[learning_rate_reduction] ) | Digit Recognizer |
1,191,702 | bin_map = {'TA':2,'Gd':3, 'Fa':1,'Ex':4,'Po':1,'None':0,'Y':1,'N':0,'Reg':3,'IR1':2,'IR2':1,'IR3':0,"None" : 0,
"No" : 2, "Mn" : 2, "Av": 3,"Gd" : 4,"Unf" : 1, "LwQ": 2, "Rec" : 3,"BLQ" : 4, "ALQ" : 5, "GLQ" : 6
}
object_columns_df['ExterQual'] = object_columns_df['ExterQual'].map(bin_map)
object_columns_df['ExterCond... | def make_submission(model, filename="submission.csv"):
df = pd.read_csv(".. /input/test.csv")
X = df.values / 255
X = X.reshape(X.shape[0], 28, 28, 1)
preds = model.predict_classes(X)
subm = pd.DataFrame(data=list(zip(range(1, len(preds)+ 1), preds)) , columns=["ImageId", "Label"])
subm.to_csv(filename, index=False... | Digit Recognizer |
1,191,702 | rest_object_columns = object_columns_df.select_dtypes(include=['object'])
object_columns_df = pd.get_dummies(object_columns_df, columns=rest_object_columns.columns )<concatenate> | make_submission(model, "submission.csv" ) | Digit Recognizer |
1,191,702 | <drop_column><EOS> | print(f"Finished in {int(time.time() - start_time)} seconds..." ) | Digit Recognizer |
52,414 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<prepare_x_and_y> | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator | Digit Recognizer |
52,414 | target= df_train['SalePrice']
df_train = df_train.drop(['SalePrice'],axis=1 )<split> | print(tf.__version__ ) | Digit Recognizer |
52,414 | x_train,x_test,y_train,y_test = train_test_split(df_train,target,test_size=0.33,random_state=0 )<choose_model_class> | train_data = pd.read_csv('.. /input/train.csv')
test_data = pd.read_csv('.. /input/test.csv' ) | Digit Recognizer |
52,414 | xgb =XGBRegressor(booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.6, gamma=0,
importance_type='gain', learning_rate=0.01, max_delta_step=0,
max_depth=4, min_child_weight=1.5, n_estimators=2400,
n_jobs=1, nthread=None, objective='reg:linear',
reg_alpha=0.6, reg_lambda=0.6, scale_pos_weight=... | train_data = train_data.values
test_data = test_data.values | Digit Recognizer |
52,414 | xgb.fit(x_train, y_train)
lgbm.fit(x_train, y_train,eval_metric='rmse' )<predict_on_test> | np.random.shuffle(train_data ) | Digit Recognizer |
52,414 | predict1 = xgb.predict(x_test)
predict = lgbm.predict(x_test )<compute_test_metric> | train_digits = train_digits / 255.0
val_digits = val_digits / 255.0
test_digits = test_digits / 255.0 | Digit Recognizer |
52,414 | print('Root Mean Square Error test = ' + str(math.sqrt(metrics.mean_squared_error(y_test, predict1))))
print('Root Mean Square Error test = ' + str(math.sqrt(metrics.mean_squared_error(y_test, predict))))<train_model> | X0 = Input(shape =(28,28,1))
X = Conv2D(filters = 32, kernel_size = 3, padding = 'Same', activation ='relu' )(X0)
X = Conv2D(filters = 32, kernel_size = 3, padding = 'Same', activation ='relu' )(X)
X = MaxPooling2D(pool_size = 2, strides = 2 )(X)
X = Dropout(0.25 )(X)
X = Conv2D(filters = 64, kernel_size = 3, paddi... | Digit Recognizer |
52,414 | xgb.fit(df_train, target)
lgbm.fit(df_train, target,eval_metric='rmse' )<predict_on_test> | model = Model(inputs=X0, outputs=Out)
model.compile(optimizer = 'adam',
loss = 'sparse_categorical_crossentropy',
metrics=['accuracy'])
initial_model_weights = model.get_weights() | Digit Recognizer |
52,414 | predict4 = lgbm.predict(df_test)
predict3 = xgb.predict(df_test)
predict_y =(predict3*0.45 + predict4 * 0.55 )<save_to_csv> | history = model.fit(train_digits, train_labels,
epochs=10,
verbose=2,
validation_data=(val_digits,val_labels)) | Digit Recognizer |
52,414 | submission = pd.DataFrame({
"Id": test["Id"],
"SalePrice": predict_y
})
submission.to_csv('submission.csv', index=False )<import_modules> | model.save("cnn1.h5" ) | Digit Recognizer |
52,414 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from lightgbm import LGBMRegressor
from xgboost import XGBRegressor
import sklearn.metrics as metrics
import math<load_from_csv> | predictions = model.predict(test_digits)
predictions = np.argmax(predictions,axis = 1)
predictions = pd.Series(predictions,name = 'Label')
ids = pd.Series(range(1,28001),name = 'ImageId')
predictions = pd.concat([predictions,ids],axis = 1)
predictions.to_csv('pred1.csv',index = False)
del(predictions)
del(ids ) | Digit Recognizer |
52,414 | sample_submission = pd.read_csv(".. /input/house-prices-advanced-regression-techniques/sample_submission.csv")
test = pd.read_csv(".. /input/house-prices-advanced-regression-techniques/test.csv")
train = pd.read_csv(".. /input/house-prices-advanced-regression-techniques/train.csv")
c_test = test.copy()
c_train = tra... | model.set_weights(initial_model_weights ) | Digit Recognizer |
52,414 | c_train['train'] = 1
c_test['train'] = 0
df = pd.concat([c_train, c_test], axis=0,sort=False )<create_dataframe> | datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
channel_shift_range=0.,
fill_mode='nearest',
... | Digit Recognizer |
52,414 | NAN = [(c, df[c].isna().mean() *100)for c in df]
NAN = pd.DataFrame(NAN, columns=["column_name", "percentage"] )<sort_values> | batch_size = 64
gen_history = model.fit_generator(datagen.flow(train_digits, train_labels, batch_size=batch_size),
epochs=10,
verbose=2,
steps_per_epoch=train_digits.shape[0]/batch_size,
validation_data=(val_digits,val_labels))
plot_history(gen_history.history ) | Digit Recognizer |
52,414 | <drop_column><EOS> | predictions = model.predict(test_digits)
predictions = np.argmax(predictions,axis = 1)
predictions = pd.Series(predictions,name = 'Label')
ids = pd.Series(range(1,28001),name = 'ImageId')
predictions = pd.concat([predictions,ids],axis = 1)
predictions.to_csv('pred2.csv',index = False)
del(predictions)
del(ids)
... | Digit Recognizer |
4,430,782 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<count_missing_values> | import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt | Digit Recognizer |
4,430,782 | null_counts = object_columns_df.isnull().sum()
print("Number of null values in each column:
{}".format(null_counts))<data_type_conversions> | training_images = pd.read_csv('.. /input/train.csv')
test_images = pd.read_csv('.. /input/test.csv')
training_labels = training_images['label']
training_images = training_images.drop(labels = ['label'], axis = 1)
training_images = training_images.values.reshape(42000, 28, 28, 1)
test_images = test_images.values.res... | Digit Recognizer |
4,430,782 | columns_None = ['BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2','GarageType','GarageFinish','GarageQual','FireplaceQu','GarageCond']
object_columns_df[columns_None]= object_columns_df[columns_None].fillna('None' )<categorify> | model = tf.keras.Sequential([
tf.keras.layers.Conv2D(32,(3, 3), activation='relu', input_shape=(28, 28, 1)) ,
tf.keras.layers.Conv2D(32,(3, 3), activation='relu', input_shape=(28, 28, 1)) ,
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Conv2D(64,(3, 3), activation='relu', input_shap... | Digit Recognizer |
4,430,782 | columns_with_lowNA = ['MSZoning','Utilities','Exterior1st','Exterior2nd','MasVnrType','Electrical','KitchenQual','Functional','SaleType']
object_columns_df[columns_with_lowNA] = object_columns_df[columns_with_lowNA].fillna(object_columns_df.mode().iloc[0] )<count_missing_values> | predict = model.predict(test_images)
predict_array = np.argmax(predict, axis=1)
predict_array = predict_array.tolist()
| Digit Recognizer |
4,430,782 | null_counts = numerical_columns_df.isnull().sum()
print("Number of null values in each column:
{}".format(null_counts))<feature_engineering> | image_set = pd.read_csv('.. /input/test.csv')
for i in np.random.randint(28001, size=10):
print("Predicted : " + str(predict_array[i]))
image = image_set.iloc[i].values.reshape(28, 28)
plt.imshow(image, cmap='gray')
plt.show() | Digit Recognizer |
4,430,782 | numerical_columns_df['GarageYrBlt'] = numerical_columns_df['GarageYrBlt'].fillna(numerical_columns_df['YrSold']-35)
numerical_columns_df['LotFrontage'] = numerical_columns_df['LotFrontage'].fillna(68 )<drop_column> | data_to_submit = pd.DataFrame({
'ImageId': range(1, 28001),
'Label': predict_array
})
data_to_submit.to_csv('csv_to_submit3.csv', index = False ) | Digit Recognizer |
4,845,532 | object_columns_df = object_columns_df.drop(['Heating','RoofMatl','Condition2','Street','Utilities'],axis=1)
<filter> | class Dataset(Dataset):
def __init__(self, path, transform=None):
self.data = pd.read_csv(path)
self.transform = transform
def __len__(self):
return len(self.data)
def __getitem__(self, index):
item = self.data.iloc[index]
image = item[1:].values.astype(np.uint8 ).reshape(( 28, 28))
label = item[0]
if self.transform ... | Digit Recognizer |
4,845,532 | Negatif = numerical_columns_df[numerical_columns_df['Age_House'] < 0]
Negatif<feature_engineering> | path = '.. /input/train.csv'
VALID_SIZE = 0.2
train_transform = transforms.Compose([
transforms.ToPILImage() ,
transforms.ToTensor() ,
transforms.Normalize(mean=(0.5,), std=(0.5,))
])
valid_transform = transforms.Compose([
transforms.ToPILImage() ,
transforms.ToTensor() ,
transforms.Normalize(mean=(0.5,), std=(0.5,))
... | Digit Recognizer |
4,845,532 | numerical_columns_df['TotalBsmtBath'] = numerical_columns_df['BsmtFullBath'] + numerical_columns_df['BsmtFullBath']*0.5
numerical_columns_df['TotalBath'] = numerical_columns_df['FullBath'] + numerical_columns_df['HalfBath']*0.5
numerical_columns_df['TotalSA']=numerical_columns_df['TotalBsmtSF'] + numerical_columns_df['... | class Net(nn.Module):
def __init__(self):
super(Net, self ).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(1, 32, 3, padding=1),
mila() ,
nn.BatchNorm2d(32),
nn.Conv2d(32, 32, 3, stride=2, padding=1),
mila() ,
nn.BatchNorm2d(32),
nn.MaxPool2d(2, 2),
nn.Dropout(0.25)
)
self.conv2 = nn.Sequential(
nn.Conv2d(32, 64, ... | Digit Recognizer |
4,845,532 | bin_map = {'TA':2,'Gd':3, 'Fa':1,'Ex':4,'Po':1,'None':0,'Y':1,'N':0,'Reg':3,'IR1':2,'IR2':1,'IR3':0,"None" : 0,
"No" : 2, "Mn" : 2, "Av": 3,"Gd" : 4,"Unf" : 1, "LwQ": 2, "Rec" : 3,"BLQ" : 4, "ALQ" : 5, "GLQ" : 6
}
object_columns_df['ExterQual'] = object_columns_df['ExterQual'].map(bin_map)
object_columns_df['ExterCond... | !wget https://raw.githubusercontent.com/LiyuanLucasLiu/RAdam/master/cifar_imagenet/utils/radam.py | Digit Recognizer |
4,845,532 | rest_object_columns = object_columns_df.select_dtypes(include=['object'])
object_columns_df = pd.get_dummies(object_columns_df, columns=rest_object_columns.columns )<concatenate> | model = Net()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = radam.RAdam(model.parameters() , lr=0.00159 ) | Digit Recognizer |
4,845,532 | df_final = pd.concat([object_columns_df, numerical_columns_df], axis=1,sort=False)
df_final.head()<drop_column> | total_epoch = 50 | Digit Recognizer |
4,845,532 | df_final = df_final.drop(['Id',],axis=1)
df_train = df_final[df_final['train'] == 1]
df_train = df_train.drop(['train',],axis=1)
df_test = df_final[df_final['train'] == 0]
df_test = df_test.drop(['SalePrice'],axis=1)
df_test = df_test.drop(['train',],axis=1 )<prepare_x_and_y> | n_epochs = total_epoch
train_loss_data,valid_loss_data = [],[]
valid_loss_min = np.Inf
class_correct = list(0.for i in range(10))
class_total = list(0.for i in range(10))
for epoch in range(n_epochs):
train_loss = 0.0
valid_loss = 0.0
model.train()
for data, target in trainloader:
data, target = data.to(device), target... | Digit Recognizer |
4,845,532 | target= df_train['SalePrice']
df_train = df_train.drop(['SalePrice'],axis=1 )<split> | model.load_state_dict(torch.load('model.pt')) | Digit Recognizer |
4,845,532 | x_train,x_test,y_train,y_test = train_test_split(df_train,target,test_size=0.33,random_state=0 )<choose_model_class> | classes = ['0', '1', '2', '3', '4',
'5', '6', '7', '8', '9']
test_loss = 0.0
class_correct = list(0.for i in range(10))
class_total = list(0.for i in range(10))
with torch.no_grad() :
model.eval()
for data, target in testloader:
data, target = data.to(device), target.to(device)
output = model(data)
loss = criterion(o... | Digit Recognizer |
4,845,532 | xgb =XGBRegressor(booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.6, gamma=0,
importance_type='gain', learning_rate=0.01, max_delta_step=0,
max_depth=4, min_child_weight=1.5, n_estimators=2400,
n_jobs=1, nthread=None, objective='reg:linear',
reg_alpha=0.6, reg_lambda=0.6, scale_pos_weight=... | class DatasetSubmissionMNIST(torch.utils.data.Dataset):
def __init__(self, file_path, transform=None):
self.data = pd.read_csv(file_path)
self.transform = transform
def __len__(self):
return len(self.data)
def __getitem__(self, index):
image = self.data.iloc[index].values.astype(np.uint8 ).reshape(( 28, 28, 1))
if se... | Digit Recognizer |
4,845,532 | xgb.fit(x_train, y_train)
lgbm.fit(x_train, y_train,eval_metric='rmse')
<predict_on_test> | transform = transforms.Compose([
transforms.ToPILImage() ,
transforms.ToTensor() ,
transforms.Normalize(mean=(0.5,), std=(0.5,))
])
submissionset = DatasetSubmissionMNIST('.. /input/test.csv', transform=transform)
submissionloader = torch.utils.data.DataLoader(submissionset, batch_size=128, shuffle=False ) | Digit Recognizer |
4,845,532 | predict1 = xgb.predict(x_test)
predict = lgbm.predict(x_test )<compute_test_metric> | submission = [['ImageId', 'Label']]
with torch.no_grad() :
model.eval()
image_id = 1
for images in submissionloader:
images = images.to(device)
log_ps = model(images)
ps = torch.exp(log_ps)
top_p, top_class = ps.topk(1, dim=1)
for prediction in top_class:
submission.append([image_id, prediction.item() ])
image_id ... | Digit Recognizer |
4,845,532 | <train_model><EOS> | with open('submission.csv', 'w')as submissionFile:
writer = csv.writer(submissionFile)
writer.writerows(submission)
print('Submission Complete!' ) | Digit Recognizer |
6,925,490 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<predict_on_test> | for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
| Digit Recognizer |
6,925,490 | predict4 = lgbm.predict(df_test)
predict3 = xgb.predict(df_test)
predict_y =(predict3*0.45 + predict4 * 0.55 )<save_to_csv> | mnist_train_complete = pd.read_csv("/kaggle/input/digit-recognizer/train.csv")
mnist_test_complete = pd.read_csv("/kaggle/input/digit-recognizer/test.csv")
mnist_train_complete.head(5 ) | Digit Recognizer |
6,925,490 | submission = pd.DataFrame({
"Id": test["Id"],
"SalePrice": predict_y
})
submission.to_csv('submission.csv', index=False )<import_modules> | train_y = mnist_train_complete.iloc[:, 0].values.astype('int32')
train_x = mnist_train_complete.iloc[:, 1:].values.astype('float32')
test_x = mnist_test_complete.values.astype('float32')
train_x = train_x.reshape(train_x.shape[0], 28, 28)
test_x = test_x.reshape(test_x.shape[0], 28, 28 ) | Digit Recognizer |
6,925,490 | import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import ModelCheckpoint
import tensorflow_hub as hub<load_from_url> | train_x = train_x.astype('float32')/np.max(train_x)
test_x = test_x.astype('float32')/np.max(test_x)
mean = np.std(train_x)
train_x -= mean
mean = np.std(test_x)
test_x -= mean | Digit Recognizer |
6,925,490 | !wget --quiet https://raw.githubusercontent.com/tensorflow/models/master/official/nlp/bert/tokenization.py<import_modules> | splitted_train_X, splitted_test_X, splitted_train_y, splitted_test_y = train_test_split(train_x, train_y, test_size=0.2, random_state=81)
ohe_splitted_train_y = tf_utils.to_categorical(splitted_train_y, 10)
ohe_splitted_test_y = tf_utils.to_categorical(splitted_test_y, 10)
print('One-hot labels:')
print(splitted_tr... | Digit Recognizer |
6,925,490 | import tokenization<categorify> | model_sol_1 = tf.keras.models.Sequential()
model_sol_1.add(tf.keras.layers.Flatten(input_shape = splitted_train_X.shape[1:]))
model_sol_1.add(tf.keras.layers.Dense(512, activation='relu'))
model_sol_1.add(tf.keras.layers.Dropout(0.2))
model_sol_1.add(tf.keras.layers.Dense(512, activation='relu'))
model_sol_1.add(tf.ker... | Digit Recognizer |
6,925,490 | def bert_encode(texts, tokenizer, max_len=512):
all_tokens = []
all_masks = []
all_segments = []
for text in texts:
text = tokenizer.tokenize(text)
text = text[:max_len-2]
input_sequence = ["[CLS]"] + text + ["[SEP]"]
pad_len = max_len - len(input_sequence)
tokens = tokenizer.convert_tokens_to_ids(input_sequence)
to... | model_sol_1.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'] ) | Digit Recognizer |
6,925,490 | def build_model(bert_layer, max_len=512):
input_word_ids = Input(shape=(max_len,), dtype=tf.int32, name="input_word_ids")
input_mask = Input(shape=(max_len,), dtype=tf.int32, name="input_mask")
segment_ids = Input(shape=(max_len,), dtype=tf.int32, name="segment_ids")
_, sequence_output = bert_layer([input_word_ids, ... | score = model_sol_1.evaluate(splitted_test_X, ohe_splitted_test_y, verbose=0)
accuracy = 100 * score[1]
print('Test accuracy: %4f%%' % accuracy ) | Digit Recognizer |
6,925,490 | train = pd.read_csv("/kaggle/input/nlp-getting-started/train.csv")
test = pd.read_csv("/kaggle/input/nlp-getting-started/test.csv")
submission = pd.read_csv("/kaggle/input/nlp-getting-started/sample_submission.csv" )<choose_model_class> | checkpointer = ModelCheckpoint(filepath='mnist.model.best.hdf5', verbose=1, save_best_only=True)
hist_sol_1 = model_sol_1.fit(splitted_train_X, ohe_splitted_train_y, batch_size=128, epochs=10,
validation_split=0.2, callbacks=[checkpointer],
verbose=2, shuffle=True ) | Digit Recognizer |
6,925,490 | %%time
module_url = "https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/1"
bert_layer = hub.KerasLayer(module_url, trainable=True )<feature_engineering> | model_sol_1.load_weights('mnist.model.best.hdf5')
score = model_sol_1.evaluate(splitted_test_X, ohe_splitted_test_y, verbose=0)
accuracy = 100 * score[1]
print('Test accuracy: %.4f%%' % accuracy ) | Digit Recognizer |
6,925,490 | vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()
do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()
tokenizer = tokenization.FullTokenizer(vocab_file, do_lower_case )<categorify> | predictions = model_sol_1.predict(test_x)
predictions = [ np.argmax(x)for x in predictions ] | Digit Recognizer |
6,925,490 | train_input = bert_encode(train.text.values, tokenizer, max_len=160)
test_input = bert_encode(test.text.values, tokenizer, max_len=160)
train_labels = train.target.values<train_model> | submission = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
submission.drop('Label', axis=1, inplace=True)
submission['Label'] = predictions
submission.to_csv('submission1.csv', index=False ) | Digit Recognizer |
6,925,490 | callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)
train_history = model.fit(
train_input, train_labels,
validation_split=0.2,
epochs=20,
batch_size=8,
callbacks=[callback]
)
model.save('model_bert.h5' )<predict_on_test> | extended_splitted_train_X = splitted_train_X[..., tf.newaxis]
extended_splitted_test_X = splitted_test_X[..., tf.newaxis]
extended_splitted_test_X.shape | Digit Recognizer |
6,925,490 | prediction= model.predict(test_input )<save_to_csv> | model_sol_2 = Sequential()
model_sol_2.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=extended_splitted_train_X.shape[1:]))
model_sol_2.add(MaxPooling2D(pool_size=2))
model_sol_2.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))
model_sol_2.add(MaxPooling2D(... | Digit Recognizer |
6,925,490 | submission['target'] = prediction.round().astype(int)
submission.to_csv('submission.csv', index=False )<train_model> | model_sol_2.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'] ) | Digit Recognizer |
6,925,490 | train_history = model.fit(
train_input, train_labels,
validation_split=0.2,
epochs=2,
batch_size=8
)
model.save('model_bert.h5' )<set_options> | score = model_sol_2.evaluate(extended_splitted_test_X, ohe_splitted_test_y, verbose=0)
accuracy = 100 * score[1]
print('Test accuracy: %4f%%' % accuracy ) | Digit Recognizer |
6,925,490 | py.init_notebook_mode(connected=True)
pio.templates.default = "plotly_dark"
pd.set_option('max_columns', 50)
<install_modules> | checkpointer = ModelCheckpoint(filepath='mnist.model.best.hdf5', verbose=1, save_best_only=True)
hist_sol_2 = model_sol_2.fit(extended_splitted_train_X, ohe_splitted_train_y, batch_size=128,
epochs=10, callbacks=[checkpointer],
verbose=2, validation_data=(extended_splitted_test_X, ohe_splitted_test_y), shuffle=True ) | Digit Recognizer |
6,925,490 | !pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.7/index.html
!pip install pytorch-pfn-extras timm<load_pretrained> | model_sol_2.load_weights('mnist.model.best.hdf5')
score = model_sol_2.evaluate(extended_splitted_test_X, ohe_splitted_test_y, verbose=0)
accuracy = 100 * score[1]
print('Test accuracy: %.4f%%' % accuracy ) | Digit Recognizer |
6,925,490 | def save_yaml(filepath: str, content: Any, width: int = 120):
with open(filepath, "w")as f:
yaml.dump(content, f, width=width )<init_hyperparams> | extended_test_x = test_x[..., tf.newaxis]
predictions = model_sol_2.predict(extended_test_x)
predictions = [ np.argmax(x)for x in predictions ]
submission = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
submission.drop('Label', axis=1, inplace=True)
submission['Label'] = predictions
submission.... | Digit Recognizer |
6,925,490 | @dataclass
class Flags:
debug: bool = True
outdir: str = "results/det"
device: str = "cuda:0"
imgdir_name: str = "vinbigdata-chest-xray-resized-png-256x256"
seed: int = 111
target_fold: int = 0
label_smoothing: float = 0.0
model_name: str = "resnet18"
model_mode: str = "normal"
epoch: int = 20
batchsize: int = 8
valid_... | image_augmentator = ImageDataGenerator(
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.2,
zoom_range=0.1,
fill_mode='nearest')
batch_size = 32
train_batches = image_augmentator.flow(extended_splitted_train_X, ohe_splitted_train_y, batch_size=batch_size)
val_batches = image_augmentato... | Digit Recognizer |
6,925,490 | flags_dict = {
"debug": False,
"outdir": "results/tmp_debug",
"imgdir_name": "vinbigdata-chest-xray-resized-png-256x256",
"model_name": "resnet18",
"num_workers": 4,
"epoch": 15,
"batchsize": 8,
"scheduler_type": "CosineAnnealingWarmRestarts",
"scheduler_kwargs": {"T_0": 28125},
"scheduler_trigger": [1, "iteration"],
"... | model_sol_3 = Sequential()
model_sol_3.add(Conv2D(filters=16, kernel_size=3, padding='same', activation='relu', input_shape=extended_splitted_train_X.shape[1:]))
model_sol_3.add(Conv2D(filters=16, kernel_size=3, padding='same', activation='relu'))
model_sol_3.add(MaxPooling2D(pool_size=2))
model_sol_3.add(Dropout(0.1))... | Digit Recognizer |
6,925,490 | print("torch", torch.__version__)
flags = Flags().update(flags_dict)
print("flags", flags)
debug = flags.debug
outdir = Path(flags.outdir)
os.makedirs(str(outdir), exist_ok=True)
flags_dict = dataclasses.asdict(flags)
save_yaml(str(outdir / "flags.yaml"), flags_dict)
inputdir = Path("/kaggle/input")
datadir = i... | model_sol_3.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'] ) | Digit Recognizer |
6,925,490 | is_normal_df = train.groupby("image_id")["class_id"].agg(lambda s:(s == 14 ).sum() ).reset_index().rename({"class_id": "num_normal_annotations"}, axis=1)
is_normal_df.head()<categorify> | checkpointer = ModelCheckpoint(filepath='mnist.model.best.hdf5', verbose=1, save_best_only=True)
hist_sol_3 = model_sol_3.fit_generator(generator=train_batches, steps_per_epoch =extended_splitted_train_X.shape[0] // batch_size,
epochs=32, callbacks=[checkpointer],
validation_data=val_batches, validation_steps=extended... | Digit Recognizer |
6,925,490 | num_normal_anno_counts_df = num_normal_anno_counts.reset_index()
num_normal_anno_counts_df["name"] = num_normal_anno_counts_df["index"].map({0: "Abnormal", 3: "Normal"})
num_normal_anno_counts_df<define_variables> | model_sol_3.load_weights('mnist.model.best.hdf5')
score = model_sol_3.evaluate(extended_splitted_test_X, ohe_splitted_test_y, verbose=0)
accuracy = 100 * score[1]
print('Test accuracy: %.4f%%' % accuracy ) | Digit Recognizer |
6,925,490 | def get_vinbigdata_dicts(
imgdir: Path,
train_df: pd.DataFrame,
train_data_type: str = "original",
use_cache: bool = True,
debug: bool = True,
target_indices: Optional[np.ndarray] = None,
):
debug_str = f"_debug{int(debug)}"
train_data_type_str = f"_{train_data_type}"
cache_path = Path(".")/ f"dataset_dicts_cache{tra... | extended_test_x = test_x[..., tf.newaxis]
predictions = model_sol_3.predict(extended_test_x)
predictions = [ np.argmax(x)for x in predictions ]
submission = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
submission.drop('Label', axis=1, inplace=True)
submission['Label'] = predictions
submission.... | Digit Recognizer |
6,925,490 |
class DatasetMixin(Dataset):
def __init__(self, transform=None):
self.transform = transform
def __getitem__(self, index):
if torch.is_tensor(index):
index = index.tolist()
if isinstance(index, slice):
current, stop, step = index.indices(len(self))
return [self.get_example_wrapper(i)for i in
six.moves.range(current,... | model_sol_4_1 = Sequential()
model_sol_4_1.add(Conv2D(filters=16, kernel_size=3, padding='same', activation='relu', input_shape=extended_splitted_train_X.shape[1:]))
model_sol_4_1.add(BatchNormalization())
model_sol_4_1.add(Conv2D(filters=16, kernel_size=3, padding='same', activation='relu'))
model_sol_4_1.add(MaxPool... | Digit Recognizer |
6,925,490 | class VinbigdataTwoClassDataset(DatasetMixin):
def __init__(self, dataset_dicts, image_transform=None, transform=None, train: bool = True,
mixup_prob: float = -1.0, label_smoothing: float = 0.0):
super(VinbigdataTwoClassDataset, self ).__init__(transform=transform)
self.dataset_dicts = dataset_dicts
self.image_transfo... | model_sol_4_1.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'] ) | Digit Recognizer |
6,925,490 | dataset_dicts = get_vinbigdata_dicts(imgdir, train, debug=debug)
dataset = VinbigdataTwoClassDataset(dataset_dicts )<normalization> | checkpointer = ModelCheckpoint(filepath='mnist.model.best.hdf5', verbose=1, save_best_only=True)
hist_sol_4 = model_sol_4_1.fit_generator(generator=train_batches, steps_per_epoch =extended_splitted_train_X.shape[0] // batch_size,
epochs=32, callbacks=[checkpointer],
validation_data=val_batches, validation_steps=extend... | Digit Recognizer |
6,925,490 | class Transform:
def __init__(
self, hflip_prob: float = 0.5, ssr_prob: float = 0.5, random_bc_prob: float = 0.5
):
self.transform = A.Compose(
[
A.HorizontalFlip(p=hflip_prob),
A.ShiftScaleRotate(
shift_limit=0.0625, scale_limit=0.1, rotate_limit=10, p=ssr_prob
),
A.RandomBrightnessContrast(p=random_bc_prob),
]
... | model_sol_4_1.load_weights('mnist.model.best.hdf5')
score = model_sol_4_1.evaluate(extended_splitted_test_X, ohe_splitted_test_y, verbose=0)
accuracy = 100 * score[1]
print('Test accuracy: %.4f%%' % accuracy ) | Digit Recognizer |
6,925,490 | aug_dataset = VinbigdataTwoClassDataset(dataset_dicts, image_transform=Transform() )<categorify> | model_sol_4_2 = Sequential()
model_sol_4_2.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=extended_splitted_train_X.shape[1:]))
model_sol_4_2.add(BatchNormalization())
model_sol_4_2.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))
model_sol_4_2.add(MaxPool... | Digit Recognizer |
6,925,490 | class Transform:
def __init__(self, aug_kwargs: Dict):
self.transform = A.Compose(
[getattr(A, name )(**kwargs)for name, kwargs in aug_kwargs.items() ]
)
def __call__(self, image):
image = self.transform(image=image)["image"]
return image<init_hyperparams> | model_sol_4_2.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'] ) | Digit Recognizer |
6,925,490 | class CNNFixedPredictor(nn.Module):
def __init__(self, cnn: nn.Module, num_classes: int = 2):
super(CNNFixedPredictor, self ).__init__()
self.cnn = cnn
self.lin = Linear(cnn.num_features, num_classes)
print("cnn.num_features", cnn.num_features)
for param in self.cnn.parameters() :
param.requires_grad = False
def forw... | checkpointer = ModelCheckpoint(filepath='mnist.model.best.hdf5', verbose=1, save_best_only=True)
hist_sol_4 = model_sol_4_2.fit_generator(generator=train_batches, steps_per_epoch=extended_splitted_train_X.shape[0] // batch_size,
epochs=32, callbacks=[checkpointer],
validation_data=val_batches, validation_steps=extende... | Digit Recognizer |
6,925,490 | def build_predictor(model_name: str, model_mode: str = "normal"):
if model_mode == "normal":
return timm.create_model(model_name, pretrained=True, num_classes=2, in_chans=3)
elif model_mode == "cnn_fixed":
timm_model = timm.create_model(model_name, pretrained=True, num_classes=0, in_chans=3)
return CNNFixedPredictor(... | model_sol_4_2.load_weights('mnist.model.best.hdf5')
score = model_sol_4_2.evaluate(extended_splitted_test_X, ohe_splitted_test_y, verbose=0)
accuracy = 100 * score[1]
print('Test accuracy: %.4f%%' % accuracy ) | Digit Recognizer |
6,925,490 | def accuracy(y: torch.Tensor, t: torch.Tensor)-> torch.Tensor:
assert y.shape[:-1] == t.shape, f"y {y.shape}, t {t.shape} is inconsistent."
pred_label = torch.max(y.detach() , dim=-1)[1]
count = t.nelement()
correct =(pred_label == t ).sum().float()
acc = correct / count
return acc
def accuracy_with_logits(y: torch.T... | extended_test_x = test_x[..., tf.newaxis]
predictions = model_sol_4_2.predict(extended_test_x)
predictions = [ np.argmax(x)for x in predictions ]
submission = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
submission.drop('Label', axis=1, inplace=True)
submission['Label'] = predictions
submissio... | Digit Recognizer |
6,925,490 | def cross_entropy_with_logits(input, target, dim=-1):
loss = torch.sum(- target * F.log_softmax(input, dim), dim)
return loss.mean()
<find_best_params> | train_y_sol5 = mnist_train_complete.iloc[:, 0].values.astype('int32')
train_x_sol5 = mnist_train_complete.iloc[:, 1:].values.astype('float32')
test_x_sol5 = mnist_test_complete.values.astype('float32')
train_x_sol5 = train_x_sol5.reshape(train_x_sol5.shape[0], 28, 28)
test_x_sol5 = test_x_sol5.reshape(test_x_sol5.s... | Digit Recognizer |
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