kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
11,129,033 | X_test = X_test[cols_list]<predict_on_test> | X_train = train.iloc[:,1:].values.astype('float32')
y_train = train['label'].values.astype('int32')
test = test.values.astype('float32')
del train | Digit Recognizer |
11,129,033 | y_test_predict=xgb_best.predict(X_test)
y_test_final = rev_trans(y_test_predict)
df_test['count']=y_test_final<save_to_csv> | X_train = X_train / 255.0
test = test / 255.0 | Digit Recognizer |
11,129,033 | df_test[['datetime','count']].to_csv('/kaggle/working/submissionNoParams.csv', index=False )<set_options> | X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
test = test.reshape(test.shape[0], 28, 28, 1 ) | Digit Recognizer |
11,129,033 | %matplotlib inline
<load_from_csv> | y_train = to_categorical(y_train)
y_train | Digit Recognizer |
11,129,033 | train_df = pd.read_csv('.. /input/bike-sharing-demand/train.csv')
test_df = pd.read_csv('.. /input/bike-sharing-demand/test.csv')
<count_missing_values> | np.random.seed(2 ) | Digit Recognizer |
11,129,033 | print(train_df.isnull().values.any() ,'
',test_df.isnull().values.any() )<create_dataframe> | X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size = 0.1, random_state = 2 ) | Digit Recognizer |
11,129,033 | test = pd.DataFrame(test_df)
test<create_dataframe> | model = Sequential()
model.add(Conv2D(filters = 64, kernel_size =(5,5), padding = 'Same', activation = 'relu', input_shape =(28,28,1)))
model.add(Conv2D(filters = 64, kernel_size =(5,5), padding = 'Same', activation = 'relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(filters = 12... | Digit Recognizer |
11,129,033 | train = pd.DataFrame(train_df)
train<feature_engineering> | model.compile(optimizer="adam", loss='categorical_crossentropy', metrics=['accuracy'] ) | Digit Recognizer |
11,129,033 | train['datetime'] = pd.to_datetime(train['datetime'], format = '%Y-%m-%dT%H:%M:%S')
train['year'] = train['datetime'].dt.year
train['month'] = train['datetime'].dt.month
train['day'] = train['datetime'].dt.day
train['hour'] = train['datetime'].dt.hour
train<drop_column> | epochs = 50
batch_size = 64 | Digit Recognizer |
11,129,033 | train = train_df.drop('datetime' , axis=1)
train<sort_values> | 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_train... | Digit Recognizer |
11,129,033 | train[["season", "count"]].groupby(['season'], as_index=False ).sum().sort_values(by='count', ascending=False )<sort_values> | history = model.fit_generator(datagen.flow(X_train,y_train, batch_size=batch_size),
epochs = epochs, validation_data =(X_test,y_test),
verbose = 2, steps_per_epoch=X_train.shape[0] // batch_size ) | Digit Recognizer |
11,129,033 | train[["holiday", "count"]].groupby(['holiday'], as_index=False ).sum().sort_values(by='count', ascending=False )<sort_values> | results = model.predict(test)
| Digit Recognizer |
11,129,033 | train[["workingday", "count"]].groupby(['workingday'], as_index=False ).sum().sort_values(by='count', ascending=False )<feature_engineering> | output = pd.concat([pd.Series(range(1,28001), name = 'ImageId'), results], axis = 1)
output | Digit Recognizer |
11,129,033 | <create_dataframe><EOS> | output.to_csv('submission.csv', index=False ) | Digit Recognizer |
11,055,227 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<feature_engineering> | %matplotlib inline
| Digit Recognizer |
11,055,227 | c=[]
for i in test['hour']:
if i>= 6 or i<= 1 :
c.append("Day")
else:
c.append("Night")
test['DayorNight']=c
test['DayorNight']=pd.factorize(test['DayorNight'])[0].reshape(-1, 1)
c=[]
test = test.drop('datetime' , axis =1)
test<feature_engineering> | train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv' ) | Digit Recognizer |
11,055,227 | c=[]
for i in train['hour']:
if i>= 6 or i<= 1 :
c.append("Day")
else:
c.append("Night")
train['DayorNight']=c
train['DayorNight']=pd.factorize(train['DayorNight'])[0].reshape(-1, 1)
c=[]
train.head(2 )<drop_column> | X_train = train.iloc[:,1:].values.astype('float32')
y_train = train['label'].values.astype('int32')
test = test.values.astype('float32')
del train | Digit Recognizer |
11,055,227 | x = train
x =x.drop('count',axis=1)
x<split> | X_train = X_train / 255.0
test = test / 255.0 | Digit Recognizer |
11,055,227 | x_train, x_val, y_train, y_val = train_test_split(x , y, train_size=0.8, test_size=0.2, random_state = 0 )<import_modules> | y_train = to_categorical(y_train)
y_train | Digit Recognizer |
11,055,227 | import xgboost as xgb
from sklearn.model_selection import cross_val_score, KFold
from sklearn.metrics import accuracy_score
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_log_error
<import_modules> | np.random.seed(2 ) | Digit Recognizer |
11,055,227 |
<compute_train_metric> | X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size = 0.0001, random_state = 2 ) | Digit Recognizer |
11,055,227 | xgbr = xgb.XGBRegressor(verbosity=0)
xgbr.fit(x_train, y_train)
score_xgbr = xgbr.score(x_train, y_train)
print("Training score: ", score_xgbr)
scores_xgbr = cross_val_score(xgbr, x_train, y_train,cv=10)
print("Mean cross-validation score: %.2f" % scores_xgbr.mean())
y_pred_xgbr = xgbr.predict(x_val)
print('Vali... | model = Sequential()
model.add(Conv2D(filters = 64, kernel_size =(5,5), padding = 'Same', activation = 'relu', input_shape =(28,28,1)))
model.add(Conv2D(filters = 64, kernel_size =(5,5), padding = 'Same', activation = 'relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(filters = 12... | Digit Recognizer |
11,055,227 |
<predict_on_test> | model.compile(optimizer="adam", loss='categorical_crossentropy', metrics=['accuracy'] ) | Digit Recognizer |
11,055,227 | pred =np.round(np.expm1(xgbr.predict(test)) ).astype(int)
pred = pd.DataFrame({"datetime": test_df["datetime"],"count": np.fix(pred)})
pred.shape<set_options> | epochs = 38
batch_size = 64 | Digit Recognizer |
11,055,227 | plt.style.use('bmh')
%matplotlib inline
<load_from_csv> | 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_train... | Digit Recognizer |
11,055,227 | train_df=pd.read_csv('.. /input/bike-sharing-demand/train.csv')
test_df=pd.read_csv('.. /input/bike-sharing-demand/test.csv')
display(train_df.head() )<count_duplicates> | history = model.fit_generator(datagen.flow(X_train,y_train, batch_size=batch_size),
epochs = epochs, validation_data =(X_test,y_test),
verbose = 2, steps_per_epoch=X_train.shape[0] // batch_size ) | Digit Recognizer |
11,055,227 | train_df.duplicated().sum()
<count_missing_values> | results = model.predict(test)
| Digit Recognizer |
11,055,227 | print(train_df.isnull().sum())
print(test_df.isnull().sum() )<feature_engineering> | output = pd.concat([pd.Series(range(1,28001), name = 'ImageId'), results], axis = 1)
output | Digit Recognizer |
11,055,227 | <feature_engineering><EOS> | output.to_csv('submission.csv', index=False ) | Digit Recognizer |
3,829,911 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<drop_column> | %matplotlib inline
np.random.seed(2)
sns.set(style='white', context='notebook', palette='deep' ) | Digit Recognizer |
3,829,911 | train_df.drop(columns = ['second','Minute'],inplace = True)
test_df.drop(columns = ['second','Minute'],inplace = True )<rename_columns> | train = pd.read_csv(".. /input/train.csv")
test = pd.read_csv(".. /input/test.csv")
submit = pd.read_csv(".. /input/sample_submission.csv" ) | Digit Recognizer |
3,829,911 | train_df = train_df.set_index('datetime')
test_df = test_df.set_index('datetime')
test_df_ID = test_df.index
train_df.head()<drop_column> | x_train = train.iloc[:,1:]
y_train = train.iloc[:,0] | Digit Recognizer |
3,829,911 | casual_df = train_df.drop(['registered','count'],axis = 1)
casual_df.head()<drop_column> | x_train=x_train/255.0
test=test/255.0 | Digit Recognizer |
3,829,911 | registered_df = train_df.drop(['casual','count'],axis = 1)
registered_df.head()<feature_engineering> | x_train = x_train.values.reshape(-1,28,28,1)
test = test.values.reshape(-1,28,28,1 ) | Digit Recognizer |
3,829,911 | registered_df['rushHours'] = registered_df['Hour'].isin([8,17,18])
<feature_engineering> | y_train=to_categorical(y_train,num_classes=10)
print(x_train.shape,y_train.shape,test.shape ) | Digit Recognizer |
3,829,911 | registered_df['registered']=np.log1p(registered_df['registered'])
registered_df['windspeed']=np.log1p(registered_df['windspeed'])
<feature_engineering> | num_classes = y_train.shape[1]
num_pixels = x_train.shape[1] | Digit Recognizer |
3,829,911 | casual_df['casual']=np.log1p(casual_df['casual'])
casual_df['windspeed']=np.log1p(casual_df['windspeed'])
<data_type_conversions> | seed=7
x_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size=0.10, random_state=seed ) | Digit Recognizer |
3,829,911 | registered_df['rushHours'] = pd.factorize(registered_df['rushHours'])[0].reshape(-1, 1)
registered_df['weekEnd'] = pd.factorize(registered_df['weekEnd'])[0].reshape(-1, 1)
casual_df['weekEnd'] = pd.factorize(casual_df['weekEnd'])[0].reshape(-1, 1)
<split> | def cnn_model() :
model = Sequential()
model.add(Conv2D(filters = 32, kernel_size =(3,3),padding = 'Same',
activation ='relu', input_shape =(28,28,1)))
model.add(Conv2D(filters = 32, kernel_size =(3,3),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.20))
model.add(Conv... | Digit Recognizer |
3,829,911 | Y_registered=registered_df.registered
registered_df.drop(columns=['registered','atemp'],inplace = True)
X_train, X_valid, y_train, y_valid = train_test_split(registered_df,Y_registered, train_size=0.8, test_size=0.2,random_state=0 )<train_model> | datagen = ImageDataGenerator(
rotation_range=10,
zoom_range = 0.1,
width_shift_range=0.1,
height_shift_range=0.1)
datagen.fit(x_train ) | Digit Recognizer |
3,829,911 | def get_best_model(X_train, X_valid, y_train, y_valid):
estimators=[('et',ExtraTreesRegressor()),('hgr', HistGradientBoostingRegressor())]
models=[RandomForestRegressor() ,AdaBoostRegressor() ,BaggingRegressor() ,SVR() ,LinearRegression() ,DecisionTreeRegressor() ,ExtraTreesRegressor() , HistGradientBoostingRegressor()... | history = model.fit_generator(datagen.flow(x_train,y_train, batch_size=batch_size),
epochs = epochs, validation_data =(x_test,y_test),
verbose = 1, steps_per_epoch=x_train.shape[0], callbacks=callbacks_list ) | Digit Recognizer |
3,829,911 | get_best_model(X_train, X_valid, y_train, y_valid )<compute_test_metric> | submit.Label =model.predict_classes(test ) | Digit Recognizer |
3,829,911 | <train_on_grid><EOS> | submit.head()
submit.to_csv('submit.csv',index=False ) | Digit Recognizer |
7,280,484 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<predict_on_test> | print("Tensorflow DL Version: " + tf.__version__)
print("Setup Completed" ) | Digit Recognizer |
7,280,484 | pred_registered = best_HistGradientAlgo_registered.predict(X_valid)
print(rmsle(pred_registered,y_valid))<split> | train_file = ".. /input/digit-recognizer/train.csv"
predict_file = ".. /input/digit-recognizer/test.csv"
submission_file = ".. /input/digit-recognizer/sample_submission.csv"
train_data = pd.read_csv(train_file, sep=',')
predict_data = pd.read_csv(predict_file, sep=',')
print("Files Preparation Completed" ) | Digit Recognizer |
7,280,484 | Y_casual=casual_df.casual
casual_df.drop(columns=['casual','atemp'],inplace = True)
X_train, X_valid, y_train, y_valid = train_test_split(casual_df,Y_casual, train_size=0.8, test_size=0.2,random_state=0 )<train_model> | print('**************************Train File Preliminary Investigation**************************')
print('1.Train File Shape:', train_data.shape)
missing_val_count_by_column =(train_data.isnull().sum())
print('2.Train File Missing Valu:', missing_val_count_by_column[missing_val_count_by_column > 0])
print(" Missing ... | Digit Recognizer |
7,280,484 | get_best_model(X_train, X_valid, y_train, y_valid )<train_on_grid> | def label_conversion(data, num_classes = 10):
label_array = np.array(data, dtype='uint8')
label_array = to_categorical(label_array,num_classes=num_classes)
return label_array
def images_conversion(data, img_row = 28, imag_col = 28):
image_array = np.array(data, dtype='uint8')
image_array = np.reshape(image_array,(le... | Digit Recognizer |
7,280,484 | HistGradientAlgo_casual = HistGradientBoostingRegressor()
param = {
'max_iter':[i for i in range(115,118)],
'max_depth' : [i for i in range(13,18)],
'max_leaf_nodes':[25]
}
gridSearch_HistGradientAlgo_casual=GridSearchCV(HistGradientAlgo_casual,param,scoring=myScorer,cv=5,verbose=3)
gridSearch_HistGradientAlgo_casual.... | y = label_conversion(train_data[output_list[0]], num_classes = num_classes)
X = images_conversion(train_data.drop(output_list[0], axis=1), img_row = img_rows, imag_col = img_cols)
X_predict = images_conversion(predict_data, img_row = img_rows, imag_col = img_cols)
print("Conversion Completed" ) | Digit Recognizer |
7,280,484 | pred_casual = best_HistGradientAlgo_casual.predict(X_valid)
print(rmsle(pred_casual,y_valid))<predict_on_test> | print("Training Data: {}
Labels: {}".format(X, y)) | Digit Recognizer |
7,280,484 | test_df['windspeed']=np.log1p(test_df['windspeed'])
test_df['rushHours'] = test_df['Hour'].isin([8,17,18])
test_df['rushHours'] = pd.factorize(test_df['rushHours'])[0].reshape(-1, 1)
test_df['weekEnd'] = pd.factorize(test_df['weekEnd'])[0].reshape(-1, 1)
test_df.drop(columns=['atemp'],inplace = True)
pred_casual =... | print("Prediction Data: {}
".format(X_predict)) | Digit Recognizer |
7,280,484 | pred_registered = pd.DataFrame(pred_registered,columns = ['count'])
pred_registered<create_dataframe> | data_aug = 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)
print("Data Au... | Digit Recognizer |
7,280,484 | pred_casual = pd.DataFrame(pred_casual,columns = ['count'])
pred_casual<prepare_output> | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)
print("Data Preparation Completed" ) | Digit Recognizer |
7,280,484 | predictions = pd.DataFrame({'datetime':test_df_ID})
predictions['count'] = pred_registered['count'] + pred_casual['count']<save_to_csv> | l_model_name = 'lenet5_model'
v_model_name = 'vgg_model'
batch_size = 64
num_classes = 10
epoch_num = 6
dropout_rate = 0.5
oz = keras.optimizers.Adam(lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=1e-4,
amsgrad=False)
sgd = keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
print("Parame... | Digit Recognizer |
7,280,484 | predictions.to_csv('submission.csv', index=False)
print("Your submission was successfully saved!" )<import_modules> | lenet5_model_history = lenet5_model.fit_generator(data_aug.flow(X_train, y_train, batch_size=batch_size),
epochs = epoch_num,
validation_data =(X_test, y_test),
verbose = 2,
steps_per_epoch=X_train.shape[0],
callbacks=[l_tb_callback,
l_lr_callback,
l_ck_callback,
l_best_callback])
lenet5_model.save(l_model_name+'.h5')... | Digit Recognizer |
7,280,484 | from IPython.display import Image<define_variables> | vgg_model_history = vgg_model.fit_generator(data_aug.flow(X_train, y_train, batch_size=batch_size),
epochs = epoch_num,
validation_data =(X_test, y_test),
verbose = 2,
steps_per_epoch=X_train.shape[0],
callbacks=[v_tb_callback,
v_lr_callback,
v_ck_callback,
v_best_callback])
vgg_model.save(v_model_name+'.h5')
del vgg... | Digit Recognizer |
7,280,484 | Image(filename=".. /input/sf-picture/sf1.jpg" )<set_options> | def best_model(histories, key='categorical_crossentropy'):
ini_flag = bool(1)
best_model = None
best_score = 0
for name, history in histories:
val_score_record = history['val_'+key]
for score in val_score_record:
if ini_flag:
best_score = score
best_model = name
ini_flag = bool(0)
if best_score > score:
best_score = ... | Digit Recognizer |
7,280,484 | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
sns.set_style("white")
rcParams['figure.figsize'] =(8,4)
<load_from_csv> | Digit Recognizer | |
7,280,484 | df = pd.read_csv(".. /input/sf-crime/train.csv.zip",dtype={"X":np.float32,"Y":np.float32} )<count_duplicates> | load_model = keras.models.load_model(best_model_name+'.h5')
print("Check Model:")
load_model.evaluate(X_test, y_test)
sample_submission = pd.read_csv(submission_file)
submission_id = sample_submission["ImageId"]
submission = pd.DataFrame({
"ImageId": submission_id,
"Label": np.argmax(load_model.predict(X_predict), ... | Digit Recognizer |
1,811,546 | print(df.duplicated(keep=False ).value_counts())
df = df.drop_duplicates()<data_type_conversions> | train = pd.read_csv('.. /input/train.csv')
labels = train.iloc[:,0].values.astype('int32')
X_train =(train.iloc[:,1:].values ).astype('float32')
X_test =(pd.read_csv('.. /input/test.csv' ).values ).astype('float32')
X_train = X_train.reshape(-1,28,28,1)
X_test = X_test.reshape(-1,28,28,1)
y_train = tf.keras.utils... | Digit Recognizer |
1,811,546 | def convert_dataframe(df):
df["Dates"] = pd.to_datetime(df["Dates"],infer_datetime_format=True)
df['Date'] = df['Dates'].dt.date
df["Year"] = df["Dates"].dt.year.astype(np.int32)
df["Month"] = df["Dates"].dt.month.astype(np.int32)
df["Day"] = df["Dates"].dt.day.astype(np.int32)
df["Hour"] = df["Dates"].dt.hour.as... | ( train_imagesRaw, train_labelsRaw),(test_imagesRaw, test_labelsRaw)= mnist.load_data() | Digit Recognizer |
1,811,546 | df_date = convert_dataframe(df )<categorify> | X_train_keras = train_imagesRaw.reshape(-1,28,28,1)
X_test_keras = test_imagesRaw.reshape(-1,28,28,1)
print("X_train_keras",X_train_keras.shape)
print("X_test_keras",X_test_keras.shape)
train_labels_keras = tf.keras.utils.to_categorical(train_labelsRaw)
test_labels_keras = tf.keras.utils.to_categorical(test_labels... | Digit Recognizer |
1,811,546 | label_cat = LabelEncoder()
df_date["Category_encode"] = label_cat.fit_transform(df_date.Category)
label_dist = LabelEncoder()
df_date["PdDistric_encode"] = label_dist.fit_transform(df_date.PdDistrict)
<filter> | train_images = np.concatenate(( X_train_keras,X_train,X_test_keras), axis=0)
print("new Concatenated train_images ", train_images.shape)
print("_"*50)
train_labels = np.concatenate(( train_labels_keras,y_train,test_labels_keras), axis=0)
print("new Concatenated train_labels ", train_labels.shape ) | Digit Recognizer |
1,811,546 | df_outliers = df_date.loc[df_date.Y < 90.].copy()<train_model> | scale = np.max(train_images)
train_images /= scale
X_test /= scale
print("Max: {}".format(scale))
| Digit Recognizer |
1,811,546 | X = df_outliers.drop(["Dates","Category","Descript","DayOfWeek","PdDistrict","Resolution","Category_encode"],axis=1 ).copy()
y = df_outliers["Category_encode"]
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.25, random_state = 21)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
... | input_size = train_images.shape
n_logits = train_labels.shape[1]
print("Input: {}".format(input_size))
print("Output: {}".format(n_logits)) | Digit Recognizer |
1,811,546 | X_train_df = pd.DataFrame(X_train)
X_val_df = pd.DataFrame(X_val)
X_train_df["Kmean"] = kmeans.labels_
X_val_df["Kmean"] = kmeans.predict(X_val )<train_model> | learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001)
epochs = 30
batch_size = 512
X_train, X_val, Y_train, Yval = train_test_split(train_images, train_labels, train_size = 0.90)
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=Fals... | Digit Recognizer |
1,811,546 | classifier = RandomForestClassifier(n_jobs = -1,random_state =50,max_depth=10,max_features="auto",min_samples_split=4)
classifier.fit(X_train_df, y_train )<predict_on_test> | model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(32, kernel_size=(3, 3),padding='same', activation='relu', input_shape=input_size[1:]))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=None))
model.add(tf.keras.layers.Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))
model... | Digit Recognizer |
1,811,546 | predict_proba = classifier.predict_proba(X_val_df)
log_loss(y_val,predict_proba )<load_from_csv> | history = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),
epochs = epochs, validation_data =(X_val,Yval),
verbose = 1, steps_per_epoch=X_train.shape[0] ) | Digit Recognizer |
1,811,546 | <categorify><EOS> | predictions = model.predict_classes(X_test, verbose=0)
print(predictions)
pd.DataFrame({"ImageId": list(range(1,len(predictions)+1)) , "Label": predictions} ).to_csv("preds.csv", index=False, header=True)
| Digit Recognizer |
6,583,149 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<normalization> | !pip install keras-tuner | Digit Recognizer |
6,583,149 | test_data_scaler = scaler.transform(test_data_final )<predict_on_test> | train = pd.read_csv('.. /input/digit-recognizer/train.csv')
labels = train.iloc[:,0].values.astype('int32')
X_train =(train.iloc[:,1:].values ).astype('float32')
X_test =(pd.read_csv('.. /input/digit-recognizer/test.csv' ).values ).astype('float32')
X_train = X_train.reshape(-1,28,28,1)
X_test = X_test.reshape(-1,... | Digit Recognizer |
6,583,149 | test_data_final = pd.DataFrame(test_data_scaler)
test_data_final["Kmean"] = kmeans.predict(test_data_final)
<predict_on_test> | ( train_imagesRaw, train_labelsRaw),(test_imagesRaw, test_labelsRaw)= mnist.load_data() | Digit Recognizer |
6,583,149 | test_data_pred_proba = classifier.predict_proba(test_data_final)
keys = label_cat.classes_
<prepare_output> | X_train_keras = train_imagesRaw.reshape(-1,28,28,1)
X_test_keras = test_imagesRaw.reshape(-1,28,28,1)
print("X_train_keras",X_train_keras.shape)
print("X_test_keras",X_test_keras.shape)
train_labels_keras = tf.keras.utils.to_categorical(train_labelsRaw)
test_labels_keras = tf.keras.utils.to_categorical(test_labels... | Digit Recognizer |
6,583,149 | result = pd.DataFrame(data=test_data_pred_proba,columns=keys)
result.head(3 )<save_to_csv> | train_images = np.concatenate(( X_train_keras,X_train,X_test_keras), axis=0)
print("new Concatenated train_images ", train_images.shape)
print("_"*50)
train_labels = np.concatenate(( train_labels_keras,y_train,test_labels_keras), axis=0)
print("new Concatenated train_labels ", train_labels.shape ) | Digit Recognizer |
6,583,149 | result.to_csv(path_or_buf="classifier_sf.csv",index=True, index_label = 'Id' )<load_from_csv> | scale = np.max(train_images)
train_images /= scale
X_test /= scale
print("Max: {}".format(scale)) | Digit Recognizer |
6,583,149 | train = pd.read_csv(".. /input/titanic/train.csv", index_col='PassengerId')
test = pd.read_csv('.. /input/titanic/test.csv', index_col='PassengerId')
train.head(10 )<sort_values> | X_train, X_val, y_train, y_val = train_test_split(train_images, train_labels, test_size=0.10 ) | Digit Recognizer |
6,583,149 | train.corr().abs() ['Survived'].sort_values(ascending=False )<count_missing_values> | input_size = X_train.shape
n_logits = y_train.shape[1]
print("Input: {}".format(input_size))
print("Output: {}".format(n_logits)) | Digit Recognizer |
6,583,149 | print(train.isnull().sum())
print('
The observation ratio of missing Cabin values is',round(train['Cabin'].isnull().sum() /len(train),2))
print('The observation ratio of missing Age values is',round(train['Age'].isnull().sum() /len(train),2))
print('
')
print(test.isnull().sum())
print('
The observation ratio of mis... | def build_model(hp):
num_layers = hp.Int('num_layers', min_value=2, max_value=16, step=2)
lr = hp.Choice('learning_rate', [1e-3, 5e-4])
inputs = layers.Input(shape=(28, 28, 1))
x = inputs
for idx in range(num_layers):
idx = str(idx)
filters = hp.Int('filters_' + idx, 32, 256, step=32, default=64)
x = layers.Conv2... | Digit Recognizer |
6,583,149 | train_copy = train.copy()
train_copy['Cabin'] = train_copy['Cabin'].apply(lambda x: 0 if pd.isnull(x)else 1 )<sort_values> | tuner = RandomSearch(
build_model,
objective='val_accuracy',
max_trials=8,
executions_per_trial=3,
directory='my_dir',
project_name='mnist')
tuner.search_space_summary() | Digit Recognizer |
6,583,149 | train.corr().abs() ['Age'].sort_values(ascending=False )<train_model> | tuner.search(X_train, y_train,
epochs=30,
validation_data=(X_val, y_val)) | Digit Recognizer |
6,583,149 | <prepare_output><EOS> | predictions_vector = model.predict(X_test, verbose=0)
predictions = np.argmax(predictions_vector,axis=1)
pd.DataFrame({"ImageId": list(range(1,len(predictions)+1)) , "Label": predictions} ).to_csv("preds.csv", index=False, header=True ) | Digit Recognizer |
7,204,871 | <feature_engineering><EOS> | import pandas as pd | Digit Recognizer |
7,204,871 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<feature_engineering> | import pandas as pd | Digit Recognizer |
7,204,871 | class feature_engineering(BaseEstimator, TransformerMixin):
def __init__(self, columns=None):
self.columns = columns
def fit(self, X, y=None, **fit_params):
return self
def transform(self, X, **transform_params):
X['Title'] = X.Name.str.split(',' ).str[1].str.split('.' ).str[0].str.strip()
X['Title'] = X['Title'].apply... | true_test = pd.read_csv(".. /input/mnist-in-csv/mnist_test.csv")
true_train = pd.read_csv(".. /input/mnist-in-csv/mnist_train.csv")
sample_submission = pd.read_csv(".. /input/digit-recognizer/sample_submission.csv")
given_test = pd.read_csv(".. /input/digit-recognizer/test.csv")
given_train = pd.read_csv(".. /input... | Digit Recognizer |
7,204,871 | cat_pipeline = Pipeline([
("imputer", SimpleImputer(strategy="most_frequent")) ,
("cat_encoder", OneHotEncoder()),
] )<feature_engineering> | cols = given_test.columns
given_test['dataset'] = 'test'
given_train['dataset'] = 'train' | Digit Recognizer |
7,204,871 | num_attribs = ["Age", "Fare","Family_Size"]
cat_attribs = [ "Sex", "Embarked","Pclass","Title","has_cabin"]
columns_trans = ColumnTransformer([
("num", num_pipeline, num_attribs),
("cat", cat_pipeline, cat_attribs),
] )<drop_column> | given_dataset = pd.concat([given_train.drop('label', axis=1), given_test] ).reset_index()
true_mnist = pd.concat([true_train, true_test] ).reset_index(drop=True)
labels = true_mnist['label'].values
true_mnist.drop('label', axis=1, inplace=True)
true_mnist.columns = cols | Digit Recognizer |
7,204,871 | full_pipeline = Pipeline([
('age_inferer', age_inferer(['Age', 'Pclass'])) ,
('feature_engineering', feature_engineering()),
('ColumnTransformer', columns_trans),
] )<prepare_x_and_y> | true_idx = true_mnist.sort_values(by=list(true_mnist.columns)).index
dataset_from = given_dataset.sort_values(by=list(true_mnist.columns)) ['dataset'].values
original_idx = given_dataset.sort_values(by=list(true_mnist.columns)) ['index'].values | Digit Recognizer |
7,204,871 | y_train = train["Survived"]
train = train.drop(columns=["Survived"])
x_train = full_pipeline.fit_transform(train)
<prepare_x_and_y> | for i in range(len(true_idx)) :
if dataset_from[i] == 'test':
sample_submission.loc[original_idx[i], 'Label'] = labels[true_idx[i]] | Digit Recognizer |
7,204,871 | <compute_test_metric><EOS> | sample_submission.to_csv('submission.csv', index=False ) | Digit Recognizer |
2,217,921 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<train_model> | %matplotlib notebook
| Digit Recognizer |
2,217,921 | final_classifier = SVC(probability=True)
final_classifier.fit(x_train, y_train)
x_test = full_pipeline.transform(test)
result = final_classifier.predict(x_test)
<save_to_csv> | dataRawTrain = pd.read_csv('.. /input/train.csv')
dataRawTest = pd.read_csv('.. /input/test.csv')
dataTrain = np.array(dataRawTrain.iloc[:,1:] ).astype('uint8')
dataTrain = dataTrain.reshape(-1, 1, 28, 28)
targetTrain = np.array(dataRawTrain.iloc[:,:1])
zz = np.zeros([len(targetTrain), 10])
for i, d in enumerate(... | Digit Recognizer |
2,217,921 | submission = pd.DataFrame({'PassengerId':test.index,'Survived':result})
submission.to_csv('submissionRF_optim_param.csv',index=False )<set_options> | class NetMNIST2(nn.Module):
flag = False
def __init__(self):
super(NetMNIST2, self ).__init__()
self.conv0 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1, bias=True)
self.relu0 = nn.PReLU()
self.pool0 = nn.MaxPool2d(2)
self.drop0 = nn.Dropout2d()
self.conv1p = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding... | Digit Recognizer |
2,217,921 | %matplotlib inline
style = "<style>svg{width: 70% !important; height: 60% !important;} </style>"
HTML(style )<load_from_csv> | use_cuda = True
device = torch.device("cuda" if use_cuda else "cpu")
net = NetMNIST2().to(device)
print(net)
optimizer = optim.Adam(net.parameters() , lr=1e-4, weight_decay=1e-5)
criterion = F.mse_loss
def trainEpoch(e):
net.train()
for i,(data, target)in enumerate(loaderTrain):
dataCUDA, targetCUDA = Variable(data... | Digit Recognizer |
2,217,921 | <count_missing_values><EOS> | packSize = 10
inputLen = dataTestT.size(0)
indexInput = np.arange(1, inputLen + 1)
result = np.zeros([inputLen],dtype=int)
print(inputLen / packSize)
for i in range(int(inputLen / packSize)) :
dataCUDA = Variable(dataTestT[i * packSize:i*packSize+packSize].cuda())
outModel = net(dataCUDA)
result[i * packSize:i*pa... | Digit Recognizer |
8,959,201 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<prepare_x_and_y> | plt.style.use('ggplot')
| Digit Recognizer |
8,959,201 | train_X = titanic_data.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis = 1)
test_X = titanic_data_test.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis = 1)
train_y = titanic_data.Survived<data_type_conversions> | train_df = pd.read_csv(".. /input/digit-recognizer/train.csv")
test_df = pd.read_csv(".. /input/digit-recognizer/test.csv" ) | Digit Recognizer |
8,959,201 | train_X = train_X.fillna({'Age': train_X.Age.median() , 'Fare': train_X.Fare.median() })
test_X = test_X.fillna({'Age': test_X.Age.median() , 'Fare': test_X.Fare.median() } )<categorify> | X =(train_df.iloc[:,1:].values ).astype('float32')
Y = train_df.iloc[:,0].values.astype('int32')
test = test_df.values.astype('float32' ) | Digit Recognizer |
8,959,201 | train_X = pd.get_dummies(train_X)
test_X = pd.get_dummies(test_X )<count_missing_values> | X = X.reshape(X.shape[0], 28, 28, 1)
Y = to_categorical(Y)
test = test.reshape(test.shape[0], 28, 28, 1 ) | Digit Recognizer |
8,959,201 | test_X.isnull().sum()<choose_model_class> | X = X.astype("float32")/ 255
test = test.astype("float32")/ 255 | Digit Recognizer |
8,959,201 | clf_rf = RandomForestClassifier(n_jobs=-1,
criterion='entropy',
min_samples_split=2,
min_samples_leaf=1)
parameters = {'n_estimators': range(3,40), 'max_depth': range(1,10)}
grid_search_cv_clf = GridSearchCV(clf_rf, parameters, cv = 5 )<train_on_grid> | X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size = 0.1, random_state=0 ) | Digit Recognizer |
8,959,201 | grid_search_cv_clf.fit(train_X,train_y )<find_best_params> | 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 |
8,959,201 | best_clf = grid_search_cv_clf.best_estimator_
grid_search_cv_clf.best_params_<compute_test_metric> | model = Sequential()
model.add(Conv2D(32, kernel_size = 4, activation="relu", input_shape=(28,28,1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size = 3, activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size = 2, activation="relu"))
model.add(MaxPo... | Digit Recognizer |
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