kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
14,445,592 | model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['acc'])
checkpoint = ModelCheckpoint(
filepath=f'resnet-{int(time.time())}.dhf5',
monitor='loss',
save_best_only=True
)
annealer = LearningRateScheduler(lambda x: 1e-3 * 0.8**x)
callbacks = [checkpoint, annealer]<train_model> | train_df = pd.read_csv('/kaggle/input/tabular-playground-series-jan-2021/train.csv')
test_df = pd.read_csv('/kaggle/input/tabular-playground-series-jan-2021/test.csv')
sub_df = pd.read_csv('/kaggle/input/tabular-playground-series-jan-2021/sample_submission.csv')
train_df.head() | Tabular Playground Series - Jan 2021 |
14,445,592 | batch_size = 64
history = model.fit(datagen.flow(X_train, y_label, batch_size=batch_size),
epochs = 30,
verbose = 1, steps_per_epoch=X_train.shape[0] // batch_size
, callbacks=callbacks, )<compute_test_metric> | feature_cols = train_df.drop(['id', 'target'], axis=1 ).columns
x = train_df[feature_cols]
y = train_df['target']
print(x.shape, y.shape ) | Tabular Playground Series - Jan 2021 |
14,445,592 | model.evaluate(X_train, y_label )<predict_on_test> | train_indexs = train_df.index
test_indexs = test_df.index
df = pd.concat(objs=[train_df, test_df], axis=0 ).reset_index(drop=True)
df = df.drop('id', axis=1)
len(train_indexs), len(test_indexs ) | Tabular Playground Series - Jan 2021 |
14,445,592 | def predict_proba(X, model, num_samples):
preds = [model(X, training=True)for _ in range(num_samples)]
return np.stack(preds ).mean(axis=0)
def predict_class(X, model, num_samples):
proba_preds = predict_proba(X, model, num_samples)
return np.argmax(proba_preds, axis=1 )<predict_on_test> | def fix_skew(features):
numerical_columns = features.select_dtypes(include=['int64','float64'] ).columns
skewed_features = features[numerical_columns].apply(lambda x: stats.skew(x)).sort_values(ascending=False)
high_skew = skewed_features[abs(skewed_features)> 0.5]
skewed_features = high_skew.index
for column in ske... | Tabular Playground Series - Jan 2021 |
14,445,592 | y_pred = predict_class(X_test, model, 10 )<save_to_csv> | param_grid = {
'n_estimators': [5, 10, 15, 20],
'max_depth': [2, 5, 7, 9]
}
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)
clf = XGBRegressor(random_state = 42)
clf.fit(x_train, y_train ) | Tabular Playground Series - Jan 2021 |
14,445,592 | res = pd.DataFrame(y_pred, columns=['Label'])
res.index = res.index + 1
res.index.rename('ImageId', inplace=True)
res.to_csv('res.csv' )<choose_model_class> | predictions = clf.predict(x_test)
errors = abs(predictions - y_test)
print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')
| Tabular Playground Series - Jan 2021 |
14,445,592 | successive_outputs = [layer.output for layer in model.layers[0:]]
visualization_model = tf.keras.models.Model(inputs = model.input, outputs = successive_outputs)
img = random.choice(X_train)
plt.imshow(img, cmap=plt.cm.binary)
plt.show()<predict_on_test> | def objective(trial,data=x,target=y):
train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.15,random_state=42)
param = {
'tree_method':'gpu_hist',
'lambda': trial.suggest_loguniform(
'lambda', 1e-3, 10.0
),
'alpha': trial.suggest_loguniform(
'alpha', 1e-3, 10.0
),
'colsample_bytree': trial... | Tabular Playground Series - Jan 2021 |
14,445,592 | successive_feature_maps = visualization_model.predict(img)
layer_names = [layer.name for layer in model.layers]
for layer_name, feature_map in zip(layer_names, successive_feature_maps):
if len(feature_map.shape)== 4:
n_features = feature_map.shape[-1]
size = feature_map.shape[1]
pic_num_per_row = n_features // 8 + 1
d... | study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=25)
print('Number of finished trials:', len(study.trials))
print('Best trial:', study.best_trial.params ) | Tabular Playground Series - Jan 2021 |
14,445,592 | y_pred = model.predict_classes(X_train )<import_modules> | study.best_params | Tabular Playground Series - Jan 2021 |
14,445,592 | from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score<compute_test_metric> | best_params = study.best_params
best_params['tree_method'] = 'gpu_hist'
best_params['random_state'] = 42
clf = XGBRegressor(**(best_params))
clf.fit(x, y ) | Tabular Playground Series - Jan 2021 |
14,445,592 | conf_max = confusion_matrix(y_label, y_pred)
conf_max<define_variables> | preds = pd.Series(clf.predict(test_df.drop('id', axis=1)) , name='target')
preds = pd.concat([test_df['id'], preds], axis=1 ) | Tabular Playground Series - Jan 2021 |
14,445,592 | <prepare_x_and_y><EOS> | preds.to_csv("submission.csv", index=False ) | Tabular Playground Series - Jan 2021 |
14,479,967 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<import_modules> | %matplotlib inline | Tabular Playground Series - Jan 2021 |
14,479,967 | import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.layers import Conv2D,MaxPooling2D,Flatten,Dense,Dropout
import matplotlib.pyplot as plt
import keras
from keras.utils.np_utils import to_categorical<load_from_csv> | !pip install --upgrade xgboost
xgb.__version__
| Tabular Playground Series - Jan 2021 |
14,479,967 | train = pd.read_csv('.. /input/digit-recognizer/train.csv')
test = pd.read_csv('.. /input/digit-recognizer/test.csv')
test1 = test.copy()<prepare_x_and_y> | shap.initjs() | Tabular Playground Series - Jan 2021 |
14,479,967 | x_train=train.drop(['label'],1)
y_train=train['label']
x_train=x_train.values.reshape(-1,28,28,1)
test=test.values.reshape(-1,28,28,1)
x_train=x_train/255
test=test/255
<choose_model_class> | train = pd.read_csv('.. /input/tabular-playground-series-jan-2021/train.csv')
test = pd.read_csv('.. /input/tabular-playground-series-jan-2021/test.csv')
sub = pd.read_csv('.. /input/tabular-playground-series-jan-2021/sample_submission.csv' ) | Tabular Playground Series - Jan 2021 |
14,479,967 | model=models.Sequential([
Conv2D(32,(5,5), activation='relu' , input_shape=(28,28,1)) ,
MaxPooling2D(pool_size=(2,2)) ,
Conv2D(64,(5,5), activation ='relu'),
MaxPooling2D(pool_size=(2,2)) ,
Dropout(0.25),
Conv2D(64,(3,3), activation ='relu'),
MaxPooling2D(pool_size=(2,2)) ,
Dropout(0.25),
Flatten() ,
Dense(64, activati... | train_oof = np.zeros(( 300000,))
test_preds = 0
train_oof.shape | Tabular Playground Series - Jan 2021 |
14,479,967 | model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model.fit(x_train, y_train, epochs=60, batch_size=64 )<save_to_csv> | Best_trial= {'lambda': 0.0030282073258141168,
'alpha': 0.01563845128469084,
'colsample_bytree': 0.55,
'subsample': 0.7,
'learning_rate': 0.01,
'max_depth': 15,
'random_state': 2020,
'min_child_weight': 257,
'tree_method':'gpu_hist',
'predictor': 'gpu_predictor'} | Tabular Playground Series - Jan 2021 |
14,479,967 | y_test = model.predict(test)
y_test = np.argmax(y_test, axis = 1)
index_list = []
for i in list(test1.index):
index_list.append(i+1)
submission_df = pd.DataFrame({
"ImageId": index_list,
"Label": y_test
})
submission_df.to_csv("submission_cnn.csv", index = False )<import_modules> | test = xgb.DMatrix(test[columns] ) | Tabular Playground Series - Jan 2021 |
14,479,967 | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from keras.utils.np_utils import to_categorical<load_from_csv> | NUM_FOLDS = 8
kf = KFold(n_splits=NUM_FOLDS, shuffle=True, random_state=0)
for f,(train_ind, val_ind)in tqdm(enumerate(kf.split(train, target))):
train_df, val_df = train.iloc[train_ind][columns], train.iloc[val_ind][columns]
train_target, val_target = target[train_ind], target[val_ind]
train_df = xgb.DMatrix(train_df... | Tabular Playground Series - Jan 2021 |
14,479,967 | train=pd.read_csv('.. /input/digit-recognizer/train.csv')
test=pd.read_csv('.. /input/digit-recognizer/test.csv' )<prepare_x_and_y> | mean_squared_error(train_oof, target, squared=False)
| Tabular Playground Series - Jan 2021 |
14,479,967 | x_train=train.drop(['label'],1)
y_train=train['label']<prepare_x_and_y> | np.save('train_oof', train_oof)
np.save('test_preds', test_preds ) | Tabular Playground Series - Jan 2021 |
14,479,967 | x_train=np.array(x_train)
test=np.array(test )<feature_engineering> | %%time
shap_preds = model.predict(test, pred_contribs=True ) | Tabular Playground Series - Jan 2021 |
14,479,967 | x_train=x_train/255
test=test/255<categorify> | test = pd.read_csv('.. /input/tabular-playground-series-jan-2021/test.csv')
| Tabular Playground Series - Jan 2021 |
14,479,967 | target=x_train.reshape(-1,28,28,1)
test=test.reshape(-1,28,28,1)
y_train=np.array(y_train)
label=to_categorical(y_train)
label.shape<import_modules> | test = xgb.DMatrix(test[columns] ) | Tabular Playground Series - Jan 2021 |
14,479,967 | from keras.models import Sequential
from keras.layers import Conv2D,MaxPooling2D,Flatten,Dense,Dropout<choose_model_class> | %%time
shap_interactions = model.predict(test, pred_interactions=True ) | Tabular Playground Series - Jan 2021 |
14,479,967 | model=Sequential([
Conv2D(32,(5,5), activation='relu' , input_shape=(28,28,1)) ,
MaxPooling2D(pool_size=(2,2)) ,
Conv2D(64,(5,5), activation ='relu'),
MaxPooling2D(pool_size=(2,2)) ,
Dropout(0.25),
Conv2D(64,(3,3), activation ='relu'),
MaxPooling2D(pool_size=(2,2)) ,
Dropout(0.25),
Flatten() ,
Dense(64, activation='rel... | del shap_interactions, shap_preds
gc.collect()
gc.collect() | Tabular Playground Series - Jan 2021 |
14,479,967 | model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'] )<train_model> | train = pd.read_csv('.. /input/tabular-playground-series-jan-2021/train.csv')
test = pd.read_csv('.. /input/tabular-playground-series-jan-2021/test.csv')
sub = pd.read_csv('.. /input/tabular-playground-series-jan-2021/sample_submission.csv' ) | Tabular Playground Series - Jan 2021 |
14,479,967 | model.fit(target,label,epochs=40,batch_size=64 )<predict_on_test> | train['cont13_cont4'] = train['cont13']*train['cont4']
train['cont13_cont11'] = train['cont13']*train['cont11']
train['cont13_cont7'] = train['cont13']*train['cont7']
train['cont13_cont2'] = train['cont13']*train['cont2']
train['cont13_cont10'] = train['cont13']*train['cont10']
test['cont13_cont4'] = test['cont13']*tes... | Tabular Playground Series - Jan 2021 |
14,479,967 | Y_pred = model.predict(test)
Y_pred_classes = np.argmax(Y_pred,axis = 1 )<load_from_csv> | test = xgb.DMatrix(test[columns] ) | Tabular Playground Series - Jan 2021 |
14,479,967 | submission_data = pd.read_csv('.. /input/digit-recognizer/sample_submission.csv' )<feature_engineering> | Best_trial= {'lambda': 0.0030282073258141168,
'alpha': 0.01563845128469084,
'colsample_bytree': 0.55,
'subsample': 0.7,
'learning_rate': 0.01,
'max_depth': 15,
'random_state': 2020,
'min_child_weight': 257,
'tree_method':'gpu_hist',
'predictor': 'gpu_predictor'} | Tabular Playground Series - Jan 2021 |
14,479,967 | submission_data['Label']=Y_pred_classes<save_to_csv> | kf = KFold(n_splits=NUM_FOLDS, shuffle=True, random_state=0)
for f,(train_ind, val_ind)in tqdm(enumerate(kf.split(train, target))):
train_df, val_df = train.iloc[train_ind][columns], train.iloc[val_ind][columns]
train_target, val_target = target[train_ind], target[val_ind]
train_df = xgb.DMatrix(train_df, label=train_... | Tabular Playground Series - Jan 2021 |
14,479,967 | submission_data.to_csv('submit.csv' ,index=False )<predict_on_test> | mean_squared_error(train_oof_2, target, squared=False)
| Tabular Playground Series - Jan 2021 |
14,479,967 | def test_output(i):
plt.imshow(x_train[i],cmap='gray')
predicted=np.argmax(model.predict(target[i].reshape(-1,28,28,1)))
actual=np.argmax(label[i])
plt.xlabel(f'predicted= {predicted} Actual= {actual}' )<import_modules> | mean_squared_error(0.6*train_oof+0.4*train_oof_2, target, squared=False)
| Tabular Playground Series - Jan 2021 |
14,479,967 | from PIL import Image, ImageGrab<predict_on_test> | np.save('train_oof_2', train_oof_2)
np.save('test_preds_2', test_preds_2 ) | Tabular Playground Series - Jan 2021 |
14,479,967 | def predict_digit1(img):
img = Image.open(img)
plt.imshow(img)
img = img.convert('L', dither=Image.NONE)
img = img.resize(( 28,28))
img = np.array(img)
img=np.invert(img)
predicted=np.argmax(model.predict(img.reshape(-1,28,28,1)))
plt.xlabel(f'Predicted= {predicted}' )<randomize_order> | sub['target'] = test_preds
sub.to_csv('submission.csv', index=False ) | Tabular Playground Series - Jan 2021 |
14,479,967 | predict_digit1('.. /input/temporary/Images/images.jfif' )<define_variables> | sub['target'] = test_preds_2
sub.to_csv('submission_2.csv', index=False ) | Tabular Playground Series - Jan 2021 |
14,479,967 | <define_variables><EOS> | sub['target'] = 0.6*test_preds+0.4*test_preds_2
sub.to_csv('submission_average.csv', index=False ) | Tabular Playground Series - Jan 2021 |
13,952,385 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<define_variables> | warnings.filterwarnings('ignore')
pd.set_option('display.max_rows', 50)
pd.set_option('display.max_columns', 50)
| Tabular Playground Series - Jan 2021 |
13,952,385 | predict_digit1('.. /input/temporary/Images/1.jpg' )<set_options> | df_train = pd.read_csv('.. /input/tabular-playground-series-jan-2021/train.csv')
df_test = pd.read_csv('.. /input/tabular-playground-series-jan-2021/test.csv')
continuous_features = [feature for feature in df_train.columns if feature.startswith('cont')]
target = 'target'
print(f'Training Set Shape = {df_train.shape}'... | Tabular Playground Series - Jan 2021 |
13,952,385 | warnings.filterwarnings('ignore')
<define_variables> | class Preprocessor:
def __init__(self, train, test, n_splits, shuffle, random_state, scaler, discretize_features, create_features):
self.train = train.copy(deep=True)
self.test = test.copy(deep=True)
self.n_splits = n_splits
self.shuffle = shuffle
self.random_state = random_state
self.scaler = scaler() if scaler else... | Tabular Playground Series - Jan 2021 |
13,952,385 | training_folder = '.. /input/cassava-leaf-disease-classification/train_images/'<train_model> | cross_validation_seed = 0
preprocessor = Preprocessor(train=df_train, test=df_test,
n_splits=5, shuffle=True, random_state=cross_validation_seed,
scaler=None,
create_features=False, discretize_features=False)
df_train_processed, df_test_processed = preprocessor.transform()
print(f'
Preprocessed Training Set Shape = {d... | Tabular Playground Series - Jan 2021 |
13,952,385 | img = Image.open(".. /input/cassava-leaf-disease-classification/train_images/1277648239.jpg")
plt.imshow(img)
plt.show()<load_from_csv> | class TreeModels:
def __init__(self, predictors, target, model, model_parameters, boosting_rounds, early_stopping_rounds, seeds):
self.predictors = predictors
self.target = target
self.model = model
self.model_parameters = model_parameters
self.boosting_rounds = boosting_rounds
self.early_stopping_rounds = early_stoppi... | Tabular Playground Series - Jan 2021 |
13,952,385 | samples_df = pd.read_csv(".. /input/cassava-leaf-disease-classification/train.csv")
samples_df = shuffle(samples_df, random_state=42)
samples_df["filepath"] = training_folder+samples_df["image_id"]
samples_df[:10]<prepare_x_and_y> | TRAIN_LGB = False
if TRAIN_LGB:
model = 'LGB'
lgb_preprocessor = Preprocessor(train=df_train, test=df_test,
n_splits=5, shuffle=True, random_state=cross_validation_seed, scaler=None,
create_features=False, discretize_features=False)
df_train_lgb, df_test_lgb = lgb_preprocessor.transform()
print(f'
{model} Training Set... | Tabular Playground Series - Jan 2021 |
13,952,385 | y=samples_df['label'].values
y = to_categorical(y )<count_unique_values> | TRAIN_CB = False
if TRAIN_CB:
model = 'CB'
cb_preprocessor = Preprocessor(train=df_train, test=df_test,
n_splits=5, shuffle=True, random_state=cross_validation_seed, scaler=None,
create_features=False, discretize_features=False)
df_train_cb, df_test_cb = cb_preprocessor.transform()
print(f'
{model} Training Set Shape ... | Tabular Playground Series - Jan 2021 |
13,952,385 | batch_size = 8
image_size = 512
input_shape =(image_size, image_size, 3)
dropout_rate = 0.4
classes_to_predict = sorted(samples_df.label.unique() )<split> | TRAIN_XGB = False
if TRAIN_XGB:
model = 'XGB'
xgb_preprocessor = Preprocessor(train=df_train, test=df_test,
n_splits=5, shuffle=True, random_state=cross_validation_seed, scaler=None,
create_features=False, discretize_features=False)
df_train_xgb, df_test_xgb = xgb_preprocessor.transform()
print(f'
{model} Training Set... | Tabular Playground Series - Jan 2021 |
13,952,385 | X_train, X_test, y_train, y_test = train_test_split(samples_df, y, random_state=42, test_size=0.2 )<prepare_x_and_y> | TRAIN_RF = False
if TRAIN_RF:
model = 'RF'
rf_preprocessor = Preprocessor(train=df_train, test=df_test,
n_splits=5, shuffle=True, random_state=cross_validation_seed, scaler=None,
create_features=False, discretize_features=False)
df_train_rf, df_test_rf = rf_preprocessor.transform()
print(f'
{model} Training Set Shape ... | Tabular Playground Series - Jan 2021 |
13,952,385 | training_data = tf.data.Dataset.from_tensor_slices(( X_train.filepath.values, y_train))
validation_data = tf.data.Dataset.from_tensor_slices(( X_test.filepath.values, y_test))<categorify> | class LinearModels:
def __init__(self, predictors, target, model, model_parameters):
self.predictors = predictors
self.target = target
self.model = model
self.model_parameters = model_parameters
def _train_and_predict_ridge_regression(self, X_train, y_train, X_test):
X = pd.concat([X_train[continuous_features], X_test[... | Tabular Playground Series - Jan 2021 |
13,952,385 | def load_image_and_label_from_path(image_path, label):
img = tf.io.read_file(image_path)
img = tf.image.decode_jpeg(img, channels=3)
return img,label
AUTOTUNE = tf.data.experimental.AUTOTUNE
training_data = training_data.map(load_image_and_label_from_path, num_parallel_calls=AUTOTUNE)
validation_data = validation_da... | FIT_RR = False
if FIT_RR:
model = 'Ridge'
ridge_preprocessor = Preprocessor(train=df_train, test=df_test,
n_splits=5, shuffle=True, random_state=cross_validation_seed, scaler=None,
create_features=False, discretize_features=False)
df_train_ridge, df_test_ridge = ridge_preprocessor.transform()
print(f'
{model} Training... | Tabular Playground Series - Jan 2021 |
13,952,385 | training_data_batches = training_data.shuffle(buffer_size=1000 ).batch(batch_size ).prefetch(buffer_size=AUTOTUNE)
validation_data_batches = validation_data.shuffle(buffer_size=1000 ).batch(batch_size ).prefetch(buffer_size=AUTOTUNE )<normalization> | FIT_SVM = False
if FIT_SVM:
model = 'SVM'
svm_preprocessor = Preprocessor(train=df_train, test=df_test,
n_splits=5, shuffle=True, random_state=cross_validation_seed, scaler=StandardScaler,
create_features=False, discretize_features=True)
df_train_svm, df_test_svm = svm_preprocessor.transform()
print(f'
{model} Trainin... | Tabular Playground Series - Jan 2021 |
13,952,385 | adapt_data = tf.data.Dataset.from_tensor_slices(X_train.filepath.values)
def adapt_mode(image_path):
img = tf.io.read_file(image_path)
img = tf.image.decode_jpeg(img, channels=3)
img = layers.experimental.preprocessing.Rescaling(1.0 / 255 )(img)
return img
adapt_data = adapt_data.map(adapt_mode, num_parallel_calls=... | class NeuralNetworks:
def __init__(self, predictors, target, model, model_parameters, seeds):
self.predictors = predictors
self.target = target
self.model = model
self.model_parameters = model_parameters
self.seeds = seeds
def _set_seed(self, seed):
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
os.... | Tabular Playground Series - Jan 2021 |
13,952,385 | data_augmentation_layers = tf.keras.Sequential(
[
layers.experimental.preprocessing.RandomCrop(height=image_size, width=image_size),
layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"),
layers.experimental.preprocessing.RandomRotation(0.25),
layers.experimental.preprocessing.RandomZoom(( -0.2, 0)) ... | TRAIN_TMLP = False
if TRAIN_TMLP:
model = 'TMLP'
tmlp_preprocessor = Preprocessor(train=df_train, test=df_test,
n_splits=5, shuffle=True, random_state=cross_validation_seed, scaler=StandardScaler,
create_features=False, discretize_features=True)
df_train_tmlp, df_test_tmlp = tmlp_preprocessor.transform()
print(f'
{mod... | Tabular Playground Series - Jan 2021 |
13,952,385 | image = Image.open(".. /input/cassava-leaf-disease-classification/train_images/1481899695.jpg")
plt.imshow(image)
plt.show()<concatenate> | TRAIN_RMLP = False
if TRAIN_RMLP:
model = 'RMLP'
rmlp_preprocessor = Preprocessor(train=df_train, test=df_test,
n_splits=5, shuffle=True, random_state=cross_validation_seed, scaler=StandardScaler,
create_features=False, discretize_features=False)
df_train_rmlp, df_test_rmlp = rmlp_preprocessor.transform()
for feature ... | Tabular Playground Series - Jan 2021 |
13,952,385 | image = tf.expand_dims(np.array(image), 0 )<normalization> | class SubmissionPipeline:
def __init__(self, train, test, blend, prediction_columns, add_public_best):
self.train = train
self.test = test
self.blend = blend
self.prediction_columns = prediction_columns
self.add_public_best = add_public_best
def weighted_average(self):
self.train['FinalPredictions'] =(0.77 * self.train... | Tabular Playground Series - Jan 2021 |
13,952,385 | <choose_model_class><EOS> | df_test_processed['target'] = df_test_submission['FinalPredictions']
df_test_processed[['id', 'target']].to_csv('submission.csv', index=False)
df_test_processed[['id', 'target']].describe() | Tabular Playground Series - Jan 2021 |
14,517,964 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<load_pretrained> | plt.style.use('fivethirtyeight')
y_ = Fore.YELLOW
r_ = Fore.RED
g_ = Fore.GREEN
b_ = Fore.BLUE
m_ = Fore.MAGENTA
c_ = Fore.CYAN
sr_ = Style.RESET_ALL
warnings.filterwarnings('ignore')
| Tabular Playground Series - Jan 2021 |
14,517,964 | %%time
model.get_layer('efficientnetb4' ).get_layer('normalization' ).adapt(adapt_data_batches )<compute_train_metric> | path = '.. /input/tabular-playground-series-jan-2021/'
train_data = pd.read_csv(path + 'train.csv')
test_data = pd.read_csv(path + 'test.csv')
sample = pd.read_csv(path + 'sample_submission.csv' ) | Tabular Playground Series - Jan 2021 |
14,517,964 | def log_t(u, t):
epsilon = 1e-7
if t == 1.0:
return tf.math.log(u + epsilon)
else:
return(u**(1.0 - t)- 1.0)/(1.0 - t)
def bi_tempered_logistic_loss(y_pred, y_true, t1, label_smoothing=0.0):
y_pred = tf.cast(y_pred, tf.float32)
y_true = tf.cast(y_true, tf.float32)
if label_smoothing > 0.0:
num_classes = tf.cast... | train_data['cont13_cont4_mul'] = train_data['cont13']*train_data['cont4']
train_data['cont13_cont11_mul'] = train_data['cont13']*train_data['cont11']
train_data['cont13_cont7_mul'] = train_data['cont13']*train_data['cont7']
train_data['cont13_cont2_mul'] = train_data['cont13']*train_data['cont2']
train_data['cont13_con... | Tabular Playground Series - Jan 2021 |
14,517,964 | epochs = 8
decay_steps = int(round(len(X_train)/batch_size)) *epochs
cosine_decay = CosineDecay(initial_learning_rate=1e-5, decay_steps=decay_steps, alpha=0.3)
callbacks = [ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]
loss = BiTemperedLogisticLoss()
model.compile(loss=loss, optim... | num_bins = int(1 + np.log2(len(train_data)))
train_data.loc[:,'bins'] = pd.cut(train_data['target'].to_numpy() ,bins=num_bins,labels=False)
features = [f'cont{x}' for x in range(1,15)]
features += [
'cont13_cont4_mul',
'cont13_cont11_mul',
'cont13_cont7_mul',
'cont13_cont2_mul',
'cont13_cont10_mul',
]
target_feature ... | Tabular Playground Series - Jan 2021 |
14,517,964 | history = model.fit(training_data_batches,
epochs = epochs,
validation_data = validation_data_batches,
callbacks = callbacks )<load_pretrained> | def rmse_score(y_true, y_pred):
return np.sqrt(mean_squared_error(y_true, y_pred)) | Tabular Playground Series - Jan 2021 |
14,517,964 | model.load_weights("best_model.h5" )<predict_on_test> | nfolds = 5
seed = 42
lgb_params={'objective':'regression',
'metrics':'rmse',
'boosting':'gbdt',
'min_data_per_group': 5,
'num_leaves': 256,
'max_depth': -1,
'learning_rate': 0.005,
'subsample_for_bin': 200000,
'lambda_l1': 1.074622455507616e-05,
'lambda_l2': 2.0521330798729704e-06,
'n_jobs': -1,
'cat_smooth': 1.0,
'ver... | Tabular Playground Series - Jan 2021 |
14,517,964 | def run_predictions_over_image_list(image_list, folder):
predictions = []
with tqdm(total=len(image_list)) as pbar:
for image_filename in image_list:
pbar.update(1)
predictions.append(predict_and_vote(image_filename, folder))
return predictions<predict_on_test> | final_preds = np.zeros(test_data.shape[0])
kfold = StratifiedKFold(n_splits=nfolds,random_state=seed)
for f,(train_idx, valid_idx)in enumerate(kfold.split(X=train_data,y=bins)) :
print(f"Fold: {f}")
X_train, X_valid, y_train, y_valid = train_data[train_idx],train_data[valid_idx],target[train_idx],target[valid_idx]
p... | Tabular Playground Series - Jan 2021 |
14,517,964 | <define_variables><EOS> | sample.target = final_preds.ravel()
sample.to_csv("submission.csv",index=False)
sample.head() | Tabular Playground Series - Jan 2021 |
14,591,678 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<feature_engineering> | !pip install --upgrade xgboost
xgb.__version__ | Tabular Playground Series - Jan 2021 |
14,591,678 | test_folder = '.. /input/cassava-leaf-disease-classification/test_images/'
submission_df = pd.DataFrame(columns={"image_id","label"})
submission_df["image_id"] = os.listdir(test_folder)
submission_df["label"] = 0<predict_on_test> | sub = pd.read_csv("/kaggle/input/tabular-playground-series-jan-2021/sample_submission.csv")
data = pd.read_csv("/kaggle/input/tabular-playground-series-jan-2021/train.csv")
final_test = pd.read_csv("/kaggle/input/tabular-playground-series-jan-2021/test.csv" ) | Tabular Playground Series - Jan 2021 |
14,591,678 | submission_df["label"] = run_predictions_over_image_list(submission_df["image_id"], test_folder )<save_to_csv> | print('Training Data')
print(data.isnull().sum())
print()
print()
print('Testing Data')
print(final_test.isnull().sum() ) | Tabular Playground Series - Jan 2021 |
14,591,678 | submission_df.to_csv("submission.csv", index=False )<define_variables> | columns = final_test.columns[1:]
train = data[columns]
target = data['target'] | Tabular Playground Series - Jan 2021 |
14,591,678 | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
<import_modules> | x_train, x_test, y_train, y_test =train_test_split(
train, target, random_state= 2021, test_size = 0.20)
xgb_initial = xgb.XGBRegressor()
xgb_initial.fit(x_train, y_train)
initial_preds = xgb_initial.predict(x_test ) | Tabular Playground Series - Jan 2021 |
14,591,678 | from datetime import datetime
from glob import glob
from scipy.ndimage.interpolation import zoom
from scipy.special import softmax
from skimage import io
from sklearn import metrics
from sklearn.metrics import log_loss
from sklearn.metrics import roc_auc_score, log_loss
from sklearn.model_selection import GroupKFold, S... | mean_squared_error(y_test, initial_preds, squared=False)
| Tabular Playground Series - Jan 2021 |
14,591,678 | CFG = {
'fold_num': 7,
'seed': 719,
'model_arch': 'tf_efficientnet_b3_ns',
'img_size': 512,
'epochs': 32,
'train_bs': 32,
'valid_bs': 32,
'lr': 1e-4,
'num_workers': 4,
'accum_iter': 1,
'verbose_step': 1,
'device': 'cuda:0',
'tta': 8
}<load_from_csv> | def objective(trial, X_data = train, Y_data = target):
x_train, x_test, y_train, y_test = train_test_split(
X_data, Y_data, random_state= 2021, test_size = 0.20)
param = {
'tree_method':'gpu_hist',
'predictor': 'gpu_predictor',
'learning_rate': trial.suggest_discrete_uniform('learning_rate',0.01,0.50,0.05),
'colsampl... | Tabular Playground Series - Jan 2021 |
14,591,678 | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
train.head()<count_values> | study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials= 100 ) | Tabular Playground Series - Jan 2021 |
14,591,678 | train.label.value_counts()<load_from_csv> | print('Number of finished trials:', len(study.trials))
print('Best trial:', study.best_trial.params)
print('Best objective value:', study.best_value)
| Tabular Playground Series - Jan 2021 |
14,591,678 | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head()<set_options> | best_trial = study.best_trial.params
best_trial['tree_method'] = 'gpu_hist'
best_trial['predictor'] = 'gpu_predictor' | Tabular Playground Series - Jan 2021 |
14,591,678 | def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
def get_img(path):
im_bgr = cv2.imread(path)
im_rgb = im_bgr[:, :, ::-1]
r... | best_trial= {'learning_rate': 0.01,
'colsample_bylevel': 0.6100000000000001,
'colsample_bytree': 0.91,
'max_depth': 10,
'subsample': 0.8,
'min_child_weight': 67,
'lambda': 0.012157425362490908,
'alpha': 7.278941365308569e-08,
'random_state': 3000,
'gamma': 1,
'tree_method': 'gpu_hist',
'predictor': 'gpu_predictor'}
| Tabular Playground Series - Jan 2021 |
14,591,678 | class CassavaDataset(Dataset):
def __init__(
self, df, data_root, transforms=None, output_label=True
):
super().__init__()
self.df = df.reset_index(drop=True ).copy()
self.transforms = transforms
self.data_root = data_root
self.output_label = output_label
def __len__(self):
return self.df.shape[0]
def __getitem__(sel... | final_test = xgb.DMatrix(final_test[columns] ) | Tabular Playground Series - Jan 2021 |
14,591,678 | HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion,
HueSaturationValue, IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur,
IAAPiecewiseAffine, RandomResizedCrop, IAASharpen, IAAEmboss,
RandomBrightnessCon... | train_oof = np.zeros(( 300000,))
test_preds = 0
train_oof.shape | Tabular Playground Series - Jan 2021 |
14,591,678 | class CassvaImgClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained)
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.mod... | NUM_FOLDS=10
kf = KFold(n_splits=NUM_FOLDS, shuffle=True, random_state=0)
fold_rmse =[]
for f,(train_ind, val_ind)in tqdm(enumerate(kf.split(train, target))):
train_df, val_df = train.iloc[train_ind][columns], train.iloc[val_ind][columns]
train_target, val_target = target[train_ind], target[val_ind]
train_df = xgb.DMa... | Tabular Playground Series - Jan 2021 |
14,591,678 | model_path = [
".. /input/cassava-10-fold-label-smoothing-02/cassava_model_10_fold_labelsmoothing_0.2_small/tf_efficientnet_b3_ns_fold_0_5",
".. /input/cassava-10-fold-label-smoothing-02/cassava_model_10_fold_labelsmoothing_0.2_small/tf_efficientnet_b3_ns_fold_1_9",
".. /input/cassava-10-fold-label-smoothing-02/cassava... | sub['target'] = test_preds
sub.to_csv('submission2_post_competition.csv', index=False ) | Tabular Playground Series - Jan 2021 |
14,591,678 | if __name__ == '__main__':
seed_everything(CFG['seed'])
tst_preds_all_folds = []
for fold in range(CFG['fold_num']):
test = pd.DataFrame()
test['image_id'] = sorted(list(
os.listdir('.. /input/cassava-leaf-disease-classification/test_images/')
))
test_ds = CassavaDataset(
test,
'.. /input/cassava-leaf-disease-classi... | def objective_2(trial, X_data = train, Y_data = target):
x_train, x_test, y_train, y_test = train_test_split(
X_data, Y_data, random_state= 2021, test_size = 0.20)
param = {
'tree_method':'gpu_hist',
'predictor': 'gpu_predictor',
'learning_rate': 0.01,
'colsample_bylevel': trial.suggest_discrete_uniform('colsample_by... | Tabular Playground Series - Jan 2021 |
14,591,678 | variable_list = %who_ls
for _ in variable_list:
if _ is not "tst_preds_all_folds":
del globals() [_]
%who_ls<import_modules> | study_2 = optuna.create_study(direction='minimize')
study_2.optimize(objective_2, n_trials= 100 ) | Tabular Playground Series - Jan 2021 |
14,591,678 | sys.path.append('.. /input/pytorch-image-models/pytorch-image-models-master')
warnings.filterwarnings('ignore')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )<define_variables> | print('Number of finished trials:', len(study_2.trials))
print('Best trial:', study_2.best_trial.params)
print('Best objective value:', study_2.best_value)
| Tabular Playground Series - Jan 2021 |
14,591,678 | OUTPUT_DIR = './'
MODEL_DIR = '.. /input/cassava-resnext/'
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
TEST_PATH = '.. /input/cassava-leaf-disease-classification/test_images'<init_hyperparams> | best_trial_2 = study_2.best_trial.params
best_trial_2['tree_method'] = 'gpu_hist'
best_trial_2['predictor'] = 'gpu_predictor'
best_trial_2['learning_rate'] = 0.01 | Tabular Playground Series - Jan 2021 |
14,591,678 | class CFG:
debug=False
num_workers=8
model_name='resnext50_32x4d'
size=512
batch_size=32
seed=2020
target_size=5
target_col='label'
n_fold=5
trn_fold=[0, 1, 2, 3, 4]
inference=True
tta=8<load_from_csv> | train_oof = np.zeros(( 300000,))
test_preds_2 = 0
train_oof.shape | Tabular Playground Series - Jan 2021 |
14,591,678 | test = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
test['filepath'] = test.image_id.apply(lambda x: os.path.join('.. /input/cassava-leaf-disease-classification/test_images', f'{x}'))
<normalization> | best_trial_2 = {'colsample_bylevel': 0.91,
'colsample_bytree': 0.6100000000000001,
'max_depth': 10,
'subsample': 0.5,
'min_child_weight': 21,
'lambda': 2.4118345076896113e-05,
'alpha': 3.234942680594196e-08,
'random_state': 3000,
'gamma': 1.51,
'tree_method': 'gpu_hist',
'predictor': 'gpu_predictor',
'learning_rate': 0... | Tabular Playground Series - Jan 2021 |
14,591,678 | def get_transforms(*, data):
if data == 'valid':
return A.Compose([
A.Resize(CFG.size, CFG.size),
A.Transpose(p=0.5),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2()
] )<choose_model_class> | NUM_FOLDS=10
kf = KFold(n_splits=NUM_FOLDS, shuffle=True, random_state=0)
fold_rmse_2 =[]
for f,(train_ind, val_ind)in tqdm(enumerate(kf.split(train, target))):
train_df, val_df = train.iloc[train_ind][columns], train.iloc[val_ind][columns]
train_target, val_target = target[train_ind], target[val_ind]
train_df = xgb.D... | Tabular Playground Series - Jan 2021 |
14,591,678 | class CustomResNext(nn.Module):
def __init__(self, model_name='resnext50_32x4d', pretrained=False):
super().__init__()
self.model = timm.create_model(model_name, pretrained=pretrained)
n_features = self.model.fc.in_features
self.model.fc = nn.Linear(n_features, CFG.target_size)
def forward(self, x):
x = self.model(x)... | sub['target'] = test_preds_2
sub.to_csv('submission3_post_competition.csv', index=False ) | Tabular Playground Series - Jan 2021 |
14,185,943 | def load_state(model_path):
model = CustomResNext(CFG.model_name, pretrained=False)
try:
model.load_state_dict(torch.load(model_path)['model'], strict=True)
state_dict = torch.load(model_path)['model']
except:
state_dict = torch.load(model_path)['model']
state_dict = {k[7:] if k.startswith('module.')else k: state_dic... | mpl.rcParams['agg.path.chunksize'] = 10000 | Tabular Playground Series - Jan 2021 |
14,185,943 | model = CustomResNext(CFG.model_name, pretrained=False)
states = [load_state(MODEL_DIR+f'{CFG.model_name}_fold{fold}.pth')for fold in CFG.trn_fold]
test_dataset = TestDataset(test, transform=get_transforms(data='valid'))
test_loader = DataLoader(test_dataset, batch_size=CFG.batch_size, shuffle=False,
num_workers=CFG.n... | train_data = pd.read_csv('/kaggle/input/tabular-playground-series-jan-2021/train.csv')
test_data = pd.read_csv('/kaggle/input/tabular-playground-series-jan-2021/test.csv')
print("successfully loaded!" ) | Tabular Playground Series - Jan 2021 |
14,185,943 | submission = test[["image_id"]]
submission["label"] =(
np.mean(tst_preds_all_folds, axis=0)* 0.7
+ predictions * 0.3
).argmax(1 )<save_to_csv> | outlier = train_data.loc[train_data.target < 1.0]
print(outlier ) | Tabular Playground Series - Jan 2021 |
14,185,943 | submission.to_csv("submission.csv", index=False )<save_to_csv> | train_data.drop([170514], inplace = True ) | Tabular Playground Series - Jan 2021 |
14,185,943 | submission.to_csv("submission.csv", index=False )<define_variables> | y_train = train_data["target"]
train_data.drop(columns = ["target"], inplace = True ) | Tabular Playground Series - Jan 2021 |
14,185,943 | class CFG:
img_size = 512
num_classes = 5
num_workers = 4
batch_size = 64
epochs = 1
OUTPUT_DIR = './'
ROOT_DIR = '.. /input/cassava-leaf-disease-classification/'
TRAIN_PATH = '.. /input/cassava-leaf-disease-classification/train_images'
TEST_PATH = '.. /input/cassava-leaf-disease-classification/test_images'
MODEL_DIR =... | params = { 'n_estimators' : [1500, 2000, 2500],
'learning_rate' : [0.01, 0.02]
}
xgb = XGBRegressor(
objective = 'reg:squarederror',
subsample = 0.8,
colsample_bytree = 0.8,
learning_rate = 0.01,
tree_method = 'gpu_hist')
grid_search = GridSearchCV(xgb,
param_grid = params,
scoring = 'neg_root_mean_squared_error',
n_... | Tabular Playground Series - Jan 2021 |
14,185,943 | def get_augmentation(data):
if data=='test':
return A.Compose([
A.Resize(CFG.img_size, CFG.img_size),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
max_pixel_value=255.0,
p=1.0),
ToTensorV2()
])
test_aug = A.Compose([
A.Resize(CFG.img_size, CFG.img_size),
A.Transpose(p=0.5),
A.HorizontalFlip(p=0... | test_data_backup = test_data.copy()
train_data.drop(columns = ["id"], inplace = True)
test_data.drop(columns = ["id"], inplace = True ) | Tabular Playground Series - Jan 2021 |
14,185,943 | class TestDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.file_names = df['image_id'].values
self.transform = transform
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
file_name = self.file_names[idx]
file_path = f'{TEST_PATH}/{file_name}'
image = Image.open(file_path ).c... | clf = XGBRegressor(
objective = 'reg:squarederror',
subsample = 0.8,
learning_rate = 0.02,
max_depth = 7,
n_estimators = 2000,
tree_method = 'gpu_hist')
clf.fit(train_data, y_train)
y_pred_xgb = clf.predict(test_data)
print(y_pred_xgb ) | Tabular Playground Series - Jan 2021 |
14,185,943 | <choose_model_class><EOS> | solution = pd.DataFrame({"id":test_data_backup.id, "target":y_pred_xgb})
solution.to_csv("solution.csv", index = False)
print("saved successful!" ) | Tabular Playground Series - Jan 2021 |
14,163,466 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<choose_model_class> | !pip3 install seaborn==0.11.0
!pip install seaborn==0.11.0 | Tabular Playground Series - Jan 2021 |
14,163,466 | class EnsembledModel() :
def __init__(self, model_paths):
super().__init__()
self.num_models = len(model_paths)
self.leafmodel1 = get_leaf_model(model_paths[0])
self.leafmodel2 = get_leaf_model(model_paths[1])
self.effb4_model1 = get_efficient_b4_model(model_paths[2])
self.effb4_model2 = get_efficient_b4_model(mode... | warnings.filterwarnings("ignore")
%matplotlib inline
red = Fore.RED
grn = Fore.GREEN
blu = Fore.BLUE
ylw = Fore.YELLOW
wht = Fore.WHITE
| Tabular Playground Series - Jan 2021 |
14,163,466 | model_paths = [
'.. /input/cassava-trained-models/with_torch_crossentropy_LeafDiseasesModel Eff-4_fold-1.pt',
'.. /input/cassava-trained-models/with_torch_crossentropy_LeafDiseasesModel Eff-4_fold-5.pt',
'.. /input/cassava-trained-models/LeafDiseasesModel Eff-4_fold-1.pt',
'.. /input/cassava-trained-models/LeafDiseases... | print(sns.__version__)
| Tabular Playground Series - Jan 2021 |
14,163,466 | def inference(model, data_loader):
epoch_preds = 0
for epoch in range(CFG.epochs):
preds = []
for images in tqdm(data_loader):
images = images.to(device)
logits = model.predict(images)
preds += [logits.softmax(1 ).detach().cpu().numpy() ]
all_img_preds = np.concatenate(preds, axis=0)
epoch_preds += all_img_preds
epo... | path = '.. /input/tabular-playground-series-jan-2021/'
train = pd.read_csv(path + 'train.csv')
test = pd.read_csv(path + 'test.csv')
sample = pd.read_csv(path + 'sample_submission.csv' ) | Tabular Playground Series - Jan 2021 |
14,163,466 | test = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
test_data = TestDataset(test, transform=get_augmentation(data='test'))
test_loader = DataLoader(test_data, batch_size=CFG.batch_size, num_workers=CFG.num_workers )<prepare_output> | print('number of null columns in train set :- ',np.sum(train.isnull().sum() > 0))
print('number of null columns in test set :-',np.sum(test.isnull().sum() > 0)) | Tabular Playground Series - Jan 2021 |
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