path
stringlengths 13
17
| screenshot_names
listlengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
121149216/cell_31
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False)
pvt = mo.pivot_table(index='model_name', values='sellers_amount', aggfunc=['min', 'mean', 'max', 'sum', 'std', 'count'])
pvt
|
code
|
121149216/cell_27
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False)
mo['best_price'].max()
|
code
|
121149216/cell_37
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False)
viz_data['release_date'].value_counts()
viz_data['release_date'].hist()
plt.xlabel('Year')
plt.ylabel('Number of releases')
plt.show()
|
code
|
49127755/cell_4
|
[
"text_plain_output_1.png"
] |
import tensorflow as tf
model = tf.keras.models.load_model('../input/cassava-abhinay/model')
model.summary()
|
code
|
49127755/cell_6
|
[
"text_plain_output_1.png"
] |
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_datagen_v2 = ImageDataGenerator(rescale=1.0 / 255)
test_dir = '../input/cassava-leaf-disease-classification/test_images/'
test = pd.DataFrame()
test['image_id'] = os.listdir('../input/cassava-leaf-disease-classification/test_images/')
test_generator = test_datagen_v2.flow_from_dataframe(test, directory=test_dir, x_col='image_id', target_size=(448, 448), batch_size=1, class_mode=None, shuffle=False)
|
code
|
49127755/cell_8
|
[
"text_plain_output_1.png"
] |
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tensorflow as tf
model = tf.keras.models.load_model('../input/cassava-abhinay/model')
model.summary()
test_datagen_v2 = ImageDataGenerator(rescale=1.0 / 255)
test_dir = '../input/cassava-leaf-disease-classification/test_images/'
test = pd.DataFrame()
test['image_id'] = os.listdir('../input/cassava-leaf-disease-classification/test_images/')
test_generator = test_datagen_v2.flow_from_dataframe(test, directory=test_dir, x_col='image_id', target_size=(448, 448), batch_size=1, class_mode=None, shuffle=False)
labels = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4}
test_generator.reset()
pred = model.predict_generator(test_generator, verbose=1, steps=len(test))
predicted_class_indices = np.argmax(pred, axis=1)
labels = dict(((v, k) for k, v in labels.items()))
predictions = [labels[k] for k in predicted_class_indices]
filenames = test_generator.filenames
results = pd.DataFrame({'image_id': filenames, 'label': predictions})
|
code
|
130011540/cell_21
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
|
code
|
130011540/cell_25
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
arr_2_d[3, 4]
|
code
|
130011540/cell_4
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
print(np.version)
print(np.__version__)
|
code
|
130011540/cell_34
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
np.sum(arr_2_d)
np.sum(arr_2_d, axis=0)
np.sum(arr_2_d, axis=1)
np.std(arr_2_d)
|
code
|
130011540/cell_23
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
print(arr_2_d)
|
code
|
130011540/cell_40
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
np.sum(arr_2_d)
np.sum(arr_2_d, axis=0)
np.sum(arr_2_d, axis=1)
np.std(arr_2_d)
arr = np.arange(1, 11)
bool_arr = arr < 5
result_arr = arr[bool_arr]
result_arr = arr[arr < 5]
print(result_arr)
|
code
|
130011540/cell_29
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
arr_2_d[:3, 1:2]
|
code
|
130011540/cell_39
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
np.sum(arr_2_d)
np.sum(arr_2_d, axis=0)
np.sum(arr_2_d, axis=1)
np.std(arr_2_d)
arr = np.arange(1, 11)
bool_arr = arr < 5
result_arr = arr[bool_arr]
print(result_arr)
|
code
|
130011540/cell_19
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
|
code
|
130011540/cell_45
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
np.sum(arr_2_d)
np.sum(arr_2_d, axis=0)
np.sum(arr_2_d, axis=1)
np.std(arr_2_d)
arr = np.arange(1, 11)
data = np.zeros((8, 8), dtype=int)
for i in range(8):
for j in range(8):
if i % 2 == 0:
if j % 2 == 0:
data[i, j] = 1
elif j % 2:
data[i, j] = 1
data = np.random.randint(1, 5, size=(10, 10))
print(data)
print()
print('Min:', np.min(data))
print('Max:', np.max(data))
|
code
|
130011540/cell_18
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
|
code
|
130011540/cell_32
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
np.sum(arr_2_d)
np.sum(arr_2_d, axis=0)
np.sum(arr_2_d, axis=1)
|
code
|
130011540/cell_8
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
print(arr_10to50)
|
code
|
130011540/cell_16
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
|
code
|
130011540/cell_38
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
np.sum(arr_2_d)
np.sum(arr_2_d, axis=0)
np.sum(arr_2_d, axis=1)
np.std(arr_2_d)
arr = np.arange(1, 11)
bool_arr = arr < 5
print(bool_arr)
|
code
|
130011540/cell_43
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
np.sum(arr_2_d)
np.sum(arr_2_d, axis=0)
np.sum(arr_2_d, axis=1)
np.std(arr_2_d)
arr = np.arange(1, 11)
data = np.zeros((8, 8), dtype=int)
for i in range(8):
for j in range(8):
if i % 2 == 0:
if j % 2 == 0:
data[i, j] = 1
elif j % 2:
data[i, j] = 1
print(data)
|
code
|
130011540/cell_31
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
np.sum(arr_2_d)
|
code
|
130011540/cell_14
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
|
code
|
130011540/cell_10
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
print(arr_10to50_even)
|
code
|
130011540/cell_27
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
arr_2_d[:3, 1:2]
|
code
|
130011540/cell_37
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).reshape(5, 5)
np.sum(arr_2_d)
np.sum(arr_2_d, axis=0)
np.sum(arr_2_d, axis=1)
np.std(arr_2_d)
arr = np.arange(1, 11)
print(arr)
|
code
|
130011540/cell_12
|
[
"text_plain_output_1.png"
] |
import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
|
code
|
50234943/cell_9
|
[
"text_plain_output_1.png"
] |
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_estimators=1000, random_state=42)
rf.fit(train_features, train_labels)
|
code
|
50234943/cell_4
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
features = pd.get_dummies(features)
features.iloc[:, 5:].head(5)
|
code
|
50234943/cell_2
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
print('The shape of our features is:', features.shape)
|
code
|
50234943/cell_11
|
[
"text_html_output_1.png"
] |
from sklearn.ensemble import RandomForestRegressor
import numpy as np
import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
features = pd.get_dummies(features)
import numpy as np
labels = np.array(features['actual'])
features = features.drop('actual', axis=1)
feature_list = list(features.columns)
features = np.array(features)
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_estimators=1000, random_state=42)
rf.fit(train_features, train_labels)
predictions = rf.predict(test_features)
errors = abs(predictions - test_labels)
mape = 100 * (errors / test_labels)
accuracy = 100 - np.mean(mape)
print('Accuracy:', round(accuracy, 2), '%.')
|
code
|
50234943/cell_1
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
features.head(5)
|
code
|
50234943/cell_7
|
[
"text_plain_output_1.png"
] |
print('Training Features Shape:', train_features.shape)
print('Training Labels Shape:', train_labels.shape)
print('Testing Features Shape:', test_features.shape)
print('Testing Labels Shape:', test_labels.shape)
|
code
|
50234943/cell_3
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
features.describe()
|
code
|
50234943/cell_10
|
[
"text_plain_output_1.png"
] |
from sklearn.ensemble import RandomForestRegressor
import numpy as np
import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
features = pd.get_dummies(features)
import numpy as np
labels = np.array(features['actual'])
features = features.drop('actual', axis=1)
feature_list = list(features.columns)
features = np.array(features)
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_estimators=1000, random_state=42)
rf.fit(train_features, train_labels)
predictions = rf.predict(test_features)
errors = abs(predictions - test_labels)
print('Mean Absolute Error:', round(np.mean(errors), 2), 'degrees.')
|
code
|
130016222/cell_13
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from datasets import load_dataset, Features, Value, Dataset
from transformers import pipeline
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dftrn = pd.read_csv('../input/nlp-getting-started/train.csv')
dftes = pd.read_csv('../input/nlp-getting-started/test.csv')
dfsub = pd.read_csv('../input/nlp-getting-started/sample_submission.csv')
dstrn = Dataset.from_pandas(dftrn, split='train')
dstes = Dataset.from_pandas(dftes, split='test')
clsfr = pipeline('text-classification', device=0)
restrn = clsfr(dstrn['text'])
pd.DataFrame(restrn)['label'].apply(lambda x: 1 if x == 'NEGATIVE' else 0).value_counts()
(pd.DataFrame(restrn)['label'].apply(lambda x: 1 if x == 'NEGATIVE' else 0) == dstrn['target']).sum()
|
code
|
130016222/cell_2
|
[
"text_plain_output_1.png"
] |
!ls ../input/nlp-getting-started
|
code
|
130016222/cell_11
|
[
"text_plain_output_1.png"
] |
from datasets import load_dataset, Features, Value, Dataset
from transformers import pipeline
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dftrn = pd.read_csv('../input/nlp-getting-started/train.csv')
dftes = pd.read_csv('../input/nlp-getting-started/test.csv')
dfsub = pd.read_csv('../input/nlp-getting-started/sample_submission.csv')
dstrn = Dataset.from_pandas(dftrn, split='train')
dstes = Dataset.from_pandas(dftes, split='test')
clsfr = pipeline('text-classification', device=0)
restrn = clsfr(dstrn['text'])
pd.DataFrame(restrn)['label'].apply(lambda x: 1 if x == 'NEGATIVE' else 0).value_counts()
|
code
|
130016222/cell_1
|
[
"text_plain_output_1.png"
] |
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
|
code
|
130016222/cell_18
|
[
"text_plain_output_1.png"
] |
!ls
|
code
|
130016222/cell_8
|
[
"text_plain_output_1.png"
] |
from transformers import pipeline
clsfr = pipeline('text-classification', device=0)
|
code
|
130016222/cell_15
|
[
"text_plain_output_1.png"
] |
from datasets import load_dataset, Features, Value, Dataset
from transformers import pipeline
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dftrn = pd.read_csv('../input/nlp-getting-started/train.csv')
dftes = pd.read_csv('../input/nlp-getting-started/test.csv')
dfsub = pd.read_csv('../input/nlp-getting-started/sample_submission.csv')
dstrn = Dataset.from_pandas(dftrn, split='train')
dstes = Dataset.from_pandas(dftes, split='test')
clsfr = pipeline('text-classification', device=0)
restrn = clsfr(dstrn['text'])
restes = clsfr(dstes['text'])
pd.DataFrame(restrn)['label'].apply(lambda x: 1 if x == 'NEGATIVE' else 0).value_counts()
(pd.DataFrame(restrn)['label'].apply(lambda x: 1 if x == 'NEGATIVE' else 0) == dstrn['target']).sum()
preds = pd.DataFrame(restes)['label'].apply(lambda x: 1 if x == 'NEGATIVE' else 0)
preds
|
code
|
130016222/cell_3
|
[
"text_plain_output_1.png"
] |
import pandas as pd
from datasets import load_dataset, Features, Value, Dataset
from transformers import pipeline
import numpy as np
import torch
|
code
|
130016222/cell_12
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from datasets import load_dataset, Features, Value, Dataset
from transformers import pipeline
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dftrn = pd.read_csv('../input/nlp-getting-started/train.csv')
dftes = pd.read_csv('../input/nlp-getting-started/test.csv')
dfsub = pd.read_csv('../input/nlp-getting-started/sample_submission.csv')
dstrn = Dataset.from_pandas(dftrn, split='train')
dstes = Dataset.from_pandas(dftes, split='test')
clsfr = pipeline('text-classification', device=0)
restrn = clsfr(dstrn['text'])
len(restrn)
|
code
|
106201490/cell_21
|
[
"text_plain_output_1.png"
] |
from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
from vncorenlp import VnCoreNLP
vncorenlp = VnCoreNLP('../input/vncorenlp/VnCoreNLP-1.1.1.jar', annotators='wseg,pos,ner,parse', max_heap_size='-Xmx2g')
vncorenlp_example = vncorenlp.tokenize('nắng chiếu lung linh trên hoa vàng')
import fasttext.util
cbow_pretrain = fasttext.load_model('../input/cbow-model/cc.vi.300.bin')
import re
def format_sentence(sentence):
sentence = re.sub(f'[{string.punctuation}\\d\n]', '', sentence)
tokens = vncorenlp.tokenize(sentence.lower())[0]
sentence = ' '.join(tokens)
embedding_sentence = cbow_pretrain.get_sentence_vector(sentence)
return embedding_sentence
"""
lấy phần text trong dataframe:
df[tên cột]: pandas.core.series.Series
df[tên cột].values: numpy.ndarray
"""
corpus = df['text'].values.tolist()
corpus = list(map(format_sentence, corpus))
print('Num_samples:', len(corpus))
|
code
|
106201490/cell_9
|
[
"text_plain_output_1.png"
] |
from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
from vncorenlp import VnCoreNLP
vncorenlp = VnCoreNLP('../input/vncorenlp/VnCoreNLP-1.1.1.jar', annotators='wseg,pos,ner,parse', max_heap_size='-Xmx2g')
vncorenlp_example = vncorenlp.tokenize('nắng chiếu lung linh trên hoa vàng')
print(vncorenlp_example)
print(np.array(vncorenlp_example).shape)
|
code
|
106201490/cell_25
|
[
"text_plain_output_1.png"
] |
from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
from vncorenlp import VnCoreNLP
vncorenlp = VnCoreNLP('../input/vncorenlp/VnCoreNLP-1.1.1.jar', annotators='wseg,pos,ner,parse', max_heap_size='-Xmx2g')
vncorenlp_example = vncorenlp.tokenize('nắng chiếu lung linh trên hoa vàng')
import fasttext.util
cbow_pretrain = fasttext.load_model('../input/cbow-model/cc.vi.300.bin')
import re
def format_sentence(sentence):
sentence = re.sub(f'[{string.punctuation}\\d\n]', '', sentence)
tokens = vncorenlp.tokenize(sentence.lower())[0]
sentence = ' '.join(tokens)
embedding_sentence = cbow_pretrain.get_sentence_vector(sentence)
return embedding_sentence
"""
lấy phần text trong dataframe:
df[tên cột]: pandas.core.series.Series
df[tên cột].values: numpy.ndarray
"""
corpus = df['text'].values.tolist()
corpus = list(map(format_sentence, corpus))
X = np.array(corpus)
Config.word_vec = X.shape[1]
max_len_sent = max([len(sent) for sent in corpus])
Config.max_sequence_length = max_len_sent
print(Config.max_sequence_length)
|
code
|
106201490/cell_4
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
print(df.shape)
df.head()
|
code
|
106201490/cell_34
|
[
"text_plain_output_1.png"
] |
from sklearn.neural_network import MLPClassifier
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
from sklearn.neural_network import MLPClassifier
mlp_classifier = MLPClassifier(learning_rate_init=0.0001, random_state=0, max_iter=300, hidden_layer_sizes=(256, 64)).fit(X_train, y_train)
print('Evaluate:')
print(f'train score: {mlp_classifier.score(X_train, y_train)}')
print(f'test socre: {mlp_classifier.score(X_test, y_test)}')
|
code
|
106201490/cell_23
|
[
"text_plain_output_1.png"
] |
!pip install tqdm
|
code
|
106201490/cell_33
|
[
"text_plain_output_1.png"
] |
from sklearn.neural_network import MLPClassifier
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
from sklearn.neural_network import MLPClassifier
mlp_classifier = MLPClassifier(learning_rate_init=0.0001, random_state=0, max_iter=300, hidden_layer_sizes=(256, 64)).fit(X_train, y_train)
|
code
|
106201490/cell_6
|
[
"text_plain_output_1.png"
] |
with open('../input/vietnamese-stopwords/vietnamese-stopwords.txt', 'r') as f:
stop_words = f.read().split('\n')
token_stop_words = ['_'.join(stop_word.split()) for stop_word in stop_words]
print(token_stop_words[:10])
|
code
|
106201490/cell_39
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from sklearn.linear_model import Perceptron
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
from sklearn.linear_model import Perceptron
perceptron = Perceptron(tol=2e-05, random_state=0)
perceptron.fit(X_train, y_train)
print('Evaluate:')
print(f'train score: {perceptron.score(X_train, y_train)}')
print(f'test socre: {perceptron.score(X_test, y_test)}')
|
code
|
106201490/cell_26
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
labels = df['label'].values
print(labels.shape)
|
code
|
106201490/cell_2
|
[
"text_plain_output_1.png"
] |
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
print(Config.seed)
|
code
|
106201490/cell_1
|
[
"text_plain_output_1.png"
] |
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
|
code
|
106201490/cell_7
|
[
"text_plain_output_1.png"
] |
# !pip3 install underthesea
!pip install vncorenlp
|
code
|
106201490/cell_18
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
print(df.head())
|
code
|
106201490/cell_32
|
[
"text_plain_output_1.png"
] |
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
|
code
|
106201490/cell_28
|
[
"text_plain_output_1.png"
] |
import collections
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
labels = df['label'].values
value_labels = list(set(labels))
import collections
count_labels = collections.Counter(labels)
observe_data = pd.DataFrame({'value_labels': value_labels, 'num_labels': [count_labels[value] for value in value_labels]})
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_axes([0.5, 0.5, 1, 1])
plt.bar(observe_data.value_labels, observe_data.num_labels, color='green')
plt.title('Thống kê số lượng trong tập data')
plt.show()
|
code
|
106201490/cell_16
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
!pip install underthesea
|
code
|
106201490/cell_38
|
[
"text_plain_output_1.png"
] |
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
|
code
|
106201490/cell_3
|
[
"text_plain_output_1.png"
] |
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
print(string.punctuation)
|
code
|
106201490/cell_17
|
[
"text_plain_output_1.png"
] |
from underthesea import word_tokenize
from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
from vncorenlp import VnCoreNLP
vncorenlp = VnCoreNLP('../input/vncorenlp/VnCoreNLP-1.1.1.jar', annotators='wseg,pos,ner,parse', max_heap_size='-Xmx2g')
vncorenlp_example = vncorenlp.tokenize('nắng chiếu lung linh trên hoa vàng')
import fasttext.util
cbow_pretrain = fasttext.load_model('../input/cbow-model/cc.vi.300.bin')
import re
def format_sentence(sentence):
sentence = re.sub(f'[{string.punctuation}\\d\n]', '', sentence)
tokens = vncorenlp.tokenize(sentence.lower())[0]
sentence = ' '.join(tokens)
embedding_sentence = cbow_pretrain.get_sentence_vector(sentence)
return embedding_sentence
from underthesea import word_tokenize
'\nSự khác biệt giữa vnnlpcore và underthesea:\n - Shape\n - vncorenlp sử dụng gạch dưới để token từ\n'
print(vncorenlp.tokenize('thậm chí ở đó chỉ có dây cáp nguồn'))
print(word_tokenize('thậm chí ở đó chỉ có dây cáp nguồn'))
|
code
|
106201490/cell_35
|
[
"text_plain_output_1.png"
] |
from tqdm import tqdm
from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
from vncorenlp import VnCoreNLP
vncorenlp = VnCoreNLP('../input/vncorenlp/VnCoreNLP-1.1.1.jar', annotators='wseg,pos,ner,parse', max_heap_size='-Xmx2g')
vncorenlp_example = vncorenlp.tokenize('nắng chiếu lung linh trên hoa vàng')
import fasttext.util
cbow_pretrain = fasttext.load_model('../input/cbow-model/cc.vi.300.bin')
import re
def format_sentence(sentence):
sentence = re.sub(f'[{string.punctuation}\\d\n]', '', sentence)
tokens = vncorenlp.tokenize(sentence.lower())[0]
sentence = ' '.join(tokens)
embedding_sentence = cbow_pretrain.get_sentence_vector(sentence)
return embedding_sentence
"""
lấy phần text trong dataframe:
df[tên cột]: pandas.core.series.Series
df[tên cột].values: numpy.ndarray
"""
corpus = df['text'].values.tolist()
corpus = list(map(format_sentence, corpus))
X = np.array(corpus)
Config.word_vec = X.shape[1]
labels = df['label'].values
Y = []
X = []
for idx in tqdm(range(len(labels))):
if labels[idx] == 0 or labels[idx] == 2:
Y.append(labels[idx])
X.append(corpus[idx])
|
code
|
106201490/cell_14
|
[
"text_plain_output_1.png"
] |
with open('../input/vietnamese-stopwords/vietnamese-stopwords.txt', 'r') as f:
stop_words = f.read().split('\n')
token_stop_words = ['_'.join(stop_word.split()) for stop_word in stop_words]
print(token_stop_words[:10])
|
code
|
106201490/cell_22
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
from vncorenlp import VnCoreNLP
vncorenlp = VnCoreNLP('../input/vncorenlp/VnCoreNLP-1.1.1.jar', annotators='wseg,pos,ner,parse', max_heap_size='-Xmx2g')
vncorenlp_example = vncorenlp.tokenize('nắng chiếu lung linh trên hoa vàng')
import fasttext.util
cbow_pretrain = fasttext.load_model('../input/cbow-model/cc.vi.300.bin')
import re
def format_sentence(sentence):
sentence = re.sub(f'[{string.punctuation}\\d\n]', '', sentence)
tokens = vncorenlp.tokenize(sentence.lower())[0]
sentence = ' '.join(tokens)
embedding_sentence = cbow_pretrain.get_sentence_vector(sentence)
return embedding_sentence
"""
lấy phần text trong dataframe:
df[tên cột]: pandas.core.series.Series
df[tên cột].values: numpy.ndarray
"""
corpus = df['text'].values.tolist()
corpus = list(map(format_sentence, corpus))
X = np.array(corpus)
Config.word_vec = X.shape[1]
print(Config.word_vec)
|
code
|
106201490/cell_27
|
[
"text_plain_output_1.png"
] |
import collections
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
labels = df['label'].values
value_labels = list(set(labels))
print(value_labels)
import collections
count_labels = collections.Counter(labels)
observe_data = pd.DataFrame({'value_labels': value_labels, 'num_labels': [count_labels[value] for value in value_labels]})
print(observe_data)
|
code
|
106201490/cell_12
|
[
"text_plain_output_1.png"
] |
import fasttext.util
cbow_pretrain = fasttext.load_model('../input/cbow-model/cc.vi.300.bin')
|
code
|
106201490/cell_5
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
with open('../input/vietnamese-stopwords/vietnamese-stopwords.txt', 'r') as f:
stop_words = f.read().split('\n')
print(len(stop_words))
print(stop_words[:10])
|
code
|
106201490/cell_36
|
[
"image_output_1.png"
] |
from tqdm import tqdm
from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
with open('../input/vietnamese-stopwords/vietnamese-stopwords.txt', 'r') as f:
stop_words = f.read().split('\n')
from vncorenlp import VnCoreNLP
vncorenlp = VnCoreNLP('../input/vncorenlp/VnCoreNLP-1.1.1.jar', annotators='wseg,pos,ner,parse', max_heap_size='-Xmx2g')
vncorenlp_example = vncorenlp.tokenize('nắng chiếu lung linh trên hoa vàng')
import fasttext.util
cbow_pretrain = fasttext.load_model('../input/cbow-model/cc.vi.300.bin')
import re
def format_sentence(sentence):
sentence = re.sub(f'[{string.punctuation}\\d\n]', '', sentence)
tokens = vncorenlp.tokenize(sentence.lower())[0]
sentence = ' '.join(tokens)
embedding_sentence = cbow_pretrain.get_sentence_vector(sentence)
return embedding_sentence
"""
lấy phần text trong dataframe:
df[tên cột]: pandas.core.series.Series
df[tên cột].values: numpy.ndarray
"""
corpus = df['text'].values.tolist()
corpus = list(map(format_sentence, corpus))
X = np.array(corpus)
Config.word_vec = X.shape[1]
max_len_sent = max([len(sent) for sent in corpus])
Config.max_sequence_length = max_len_sent
labels = df['label'].values
class GenData:
def __init__(self, stop_words, num_sentence, list_sentence):
self.stop_words = stop_words
self.num_sentence = num_sentence
self.list_sentence = list_sentence
def __gen_new_item__(self):
num_sentece_gen = len(self.list_sentence) - self.num_sentence
np.random.seed(Config.seed)
np.random.shuffle(self.stop_words)
stop_words_gen = self.stop_words[:num_sentence_gen]
np.random.shuffle(self.list_sentence)
sentence_gen = self.list_sentence[:num_sentence_gen]
new_sentences = [[stop_words_gen[idx]] + sentence_gen[idx] for idx in range(num_sentencce_gen)]
return new_sentences
Y = []
X = []
for idx in tqdm(range(len(labels))):
if labels[idx] == 0 or labels[idx] == 2:
Y.append(labels[idx])
X.append(corpus[idx])
X = np.array(X)
Y = np.array(Y)
(X.shape, Y.shape)
|
code
|
74054244/cell_21
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
data_zom.rename(columns={'Known_For2': 'Restaurant speciality', 'Known_For22': 'Customer feedback'}, inplace=True)
data_zom = data_zom.drop(['Latitude', 'Longitude'], axis=1)
data_zom.isnull().sum()
data_zom.fillna(data_zom.select_dtypes(include='object').mode().iloc[0], inplace=True)
data_zom = data_zom.fillna(data_zom.mean())
plt.hist(data_zom['Dining_Rating'])
plt.title('Average rating')
plt.show()
|
code
|
74054244/cell_13
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
data_zom.rename(columns={'Known_For2': 'Restaurant speciality', 'Known_For22': 'Customer feedback'}, inplace=True)
data_zom = data_zom.drop(['Latitude', 'Longitude'], axis=1)
data_zom['Locality'].value_counts()
|
code
|
74054244/cell_9
|
[
"image_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
print('The numerical feaures are ', num_fea)
print('The categorical features are ', cat_fea)
|
code
|
74054244/cell_11
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
data_zom.rename(columns={'Known_For2': 'Restaurant speciality', 'Known_For22': 'Customer feedback'}, inplace=True)
data_zom.head()
|
code
|
74054244/cell_19
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
data_zom.rename(columns={'Known_For2': 'Restaurant speciality', 'Known_For22': 'Customer feedback'}, inplace=True)
data_zom = data_zom.drop(['Latitude', 'Longitude'], axis=1)
data_zom.isnull().sum()
data_zom.fillna(data_zom.select_dtypes(include='object').mode().iloc[0], inplace=True)
data_zom = data_zom.fillna(data_zom.mean())
plt.hist(data_zom['Pricing_for_2'])
plt.title('Average price for two')
plt.show()
|
code
|
74054244/cell_1
|
[
"text_plain_output_1.png"
] |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
|
code
|
74054244/cell_7
|
[
"image_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
data_zom.info()
|
code
|
74054244/cell_15
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
data_zom.rename(columns={'Known_For2': 'Restaurant speciality', 'Known_For22': 'Customer feedback'}, inplace=True)
data_zom = data_zom.drop(['Latitude', 'Longitude'], axis=1)
data_zom.isnull().sum()
|
code
|
74054244/cell_17
|
[
"text_html_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
data_zom.rename(columns={'Known_For2': 'Restaurant speciality', 'Known_For22': 'Customer feedback'}, inplace=True)
data_zom = data_zom.drop(['Latitude', 'Longitude'], axis=1)
data_zom.isnull().sum()
data_zom.fillna(data_zom.select_dtypes(include='object').mode().iloc[0], inplace=True)
data_zom = data_zom.fillna(data_zom.mean())
data_zom['Pricing_for_2'].describe()
|
code
|
74054244/cell_5
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
print('Size of dataset is: ', data_zom.shape)
data_zom.head()
|
code
|
34150823/cell_9
|
[
"text_plain_output_1.png"
] |
import numpy as np
import os
import random
import torch
import warnings
import os
import cv2
from PIL import Image
import time
import copy
import warnings
import random
import numpy as np
import pandas as pd
from tqdm import tqdm_notebook as tqdm
from torch.optim.lr_scheduler import ReduceLROnPlateau
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.nn import functional as F
import torchvision
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader, Dataset, sampler
from matplotlib import pyplot as plt
import torchvision.transforms as transforms
from albumentations import HorizontalFlip, VerticalFlip, ShiftScaleRotate, Normalize, Resize, Compose, GaussNoise, RandomRotate90, Transpose, RandomBrightnessContrast, RandomCrop
from albumentations.pytorch import ToTensor
import albumentations as albu
import matplotlib.image as mpi
from sklearn.metrics import f1_score
warnings.filterwarnings('ignore')
seed = 69
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device
|
code
|
34150823/cell_4
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] |
import pandas as pd
labels = pd.read_csv('/kaggle/input/oxford-iiit/labels')
labels.shape
|
code
|
34150823/cell_20
|
[
"text_plain_output_1.png"
] |
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader, Dataset, sampler
from torchvision import models
import numpy as np
import os
import pandas as pd
import random
import time
import torch
import torch.nn as nn
import warnings
import os
import cv2
from PIL import Image
import time
import copy
import warnings
import random
import numpy as np
import pandas as pd
from tqdm import tqdm_notebook as tqdm
from torch.optim.lr_scheduler import ReduceLROnPlateau
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.nn import functional as F
import torchvision
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader, Dataset, sampler
from matplotlib import pyplot as plt
import torchvision.transforms as transforms
from albumentations import HorizontalFlip, VerticalFlip, ShiftScaleRotate, Normalize, Resize, Compose, GaussNoise, RandomRotate90, Transpose, RandomBrightnessContrast, RandomCrop
from albumentations.pytorch import ToTensor
import albumentations as albu
import matplotlib.image as mpi
from sklearn.metrics import f1_score
warnings.filterwarnings('ignore')
seed = 69
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
labels = pd.read_csv('/kaggle/input/oxford-iiit/labels')
labels.shape
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device
testdata = DogandCat(test_df, phase='val')
testloader = DataLoader(testdata, batch_size=16)
from torchvision import models
resnet = models.resnet18(pretrained=True, progress=True)
for param in resnet.parameters():
param.requires_grad = False
fc_inputs = resnet.fc.in_features
resnet.fc = nn.Linear(fc_inputs, 37)
from torch.optim import lr_scheduler
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(resnet.fc.parameters(), lr=0.001, betas=(0.9, 0.999), weight_decay=0.001)
scheduler = ReduceLROnPlateau(optimizer, factor=0.33, mode='min', patience=2)
resnet.load_state_dict(torch.load('/kaggle/input/oxford-iiit/best_weights.pth'))
resnet.to(device)
corrects = 0
since = time.time()
with torch.no_grad():
for inputs, labels in testloader:
inputs = inputs.to(device)
labels = labels.to(device)
output = resnet(inputs)
_, pred = torch.max(output, 1)
corrects += torch.sum(pred == labels.data)
accuracy = corrects.cpu().numpy() / len(testloader.dataset)
time_elapsed = time.time() - since
print('Accuracy: {:4f}'.format(accuracy))
|
code
|
34150823/cell_6
|
[
"text_plain_output_1.png"
] |
import pandas as pd
labels = pd.read_csv('/kaggle/input/oxford-iiit/labels')
labels.shape
classes = labels['breed'].unique()
classes
|
code
|
34150823/cell_19
|
[
"text_html_output_1.png"
] |
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader, Dataset, sampler
from torchvision import models
import numpy as np
import os
import pandas as pd
import random
import time
import torch
import torch.nn as nn
import warnings
import os
import cv2
from PIL import Image
import time
import copy
import warnings
import random
import numpy as np
import pandas as pd
from tqdm import tqdm_notebook as tqdm
from torch.optim.lr_scheduler import ReduceLROnPlateau
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.nn import functional as F
import torchvision
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader, Dataset, sampler
from matplotlib import pyplot as plt
import torchvision.transforms as transforms
from albumentations import HorizontalFlip, VerticalFlip, ShiftScaleRotate, Normalize, Resize, Compose, GaussNoise, RandomRotate90, Transpose, RandomBrightnessContrast, RandomCrop
from albumentations.pytorch import ToTensor
import albumentations as albu
import matplotlib.image as mpi
from sklearn.metrics import f1_score
warnings.filterwarnings('ignore')
seed = 69
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
labels = pd.read_csv('/kaggle/input/oxford-iiit/labels')
labels.shape
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device
testdata = DogandCat(test_df, phase='val')
testloader = DataLoader(testdata, batch_size=16)
from torchvision import models
resnet = models.resnet18(pretrained=True, progress=True)
for param in resnet.parameters():
param.requires_grad = False
fc_inputs = resnet.fc.in_features
resnet.fc = nn.Linear(fc_inputs, 37)
from torch.optim import lr_scheduler
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(resnet.fc.parameters(), lr=0.001, betas=(0.9, 0.999), weight_decay=0.001)
scheduler = ReduceLROnPlateau(optimizer, factor=0.33, mode='min', patience=2)
resnet.load_state_dict(torch.load('/kaggle/input/oxford-iiit/best_weights.pth'))
resnet.to(device)
corrects = 0
since = time.time()
with torch.no_grad():
for inputs, labels in testloader:
inputs = inputs.to(device)
labels = labels.to(device)
output = resnet(inputs)
_, pred = torch.max(output, 1)
corrects += torch.sum(pred == labels.data)
accuracy = corrects.cpu().numpy() / len(testloader.dataset)
print()
time_elapsed = time.time() - since
print('Run complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
|
code
|
34150823/cell_7
|
[
"text_plain_output_1.png"
] |
import pandas as pd
labels = pd.read_csv('/kaggle/input/oxford-iiit/labels')
labels.shape
df1 = labels['breed']
df2 = labels['label_id']
df1 = pd.get_dummies(df1)
df = pd.concat([df2, df1], axis=1)
df.head()
|
code
|
34150823/cell_18
|
[
"text_plain_output_1.png"
] |
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torchvision import models
import numpy as np
import os
import random
import torch
import torch.nn as nn
import warnings
import os
import cv2
from PIL import Image
import time
import copy
import warnings
import random
import numpy as np
import pandas as pd
from tqdm import tqdm_notebook as tqdm
from torch.optim.lr_scheduler import ReduceLROnPlateau
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.nn import functional as F
import torchvision
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader, Dataset, sampler
from matplotlib import pyplot as plt
import torchvision.transforms as transforms
from albumentations import HorizontalFlip, VerticalFlip, ShiftScaleRotate, Normalize, Resize, Compose, GaussNoise, RandomRotate90, Transpose, RandomBrightnessContrast, RandomCrop
from albumentations.pytorch import ToTensor
import albumentations as albu
import matplotlib.image as mpi
from sklearn.metrics import f1_score
warnings.filterwarnings('ignore')
seed = 69
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device
from torchvision import models
resnet = models.resnet18(pretrained=True, progress=True)
for param in resnet.parameters():
param.requires_grad = False
fc_inputs = resnet.fc.in_features
resnet.fc = nn.Linear(fc_inputs, 37)
from torch.optim import lr_scheduler
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(resnet.fc.parameters(), lr=0.001, betas=(0.9, 0.999), weight_decay=0.001)
scheduler = ReduceLROnPlateau(optimizer, factor=0.33, mode='min', patience=2)
resnet.load_state_dict(torch.load('/kaggle/input/oxford-iiit/best_weights.pth'))
resnet.to(device)
|
code
|
34150823/cell_15
|
[
"text_plain_output_1.png"
] |
from torchvision import models
from torchvision import models
resnet = models.resnet18(pretrained=True, progress=True)
|
code
|
34150823/cell_3
|
[
"image_output_1.png"
] |
import pandas as pd
labels = pd.read_csv('/kaggle/input/oxford-iiit/labels')
labels.head()
|
code
|
34150823/cell_14
|
[
"text_html_output_1.png"
] |
from albumentations.pytorch import ToTensor
from torch.utils.data import DataLoader, Dataset, sampler
import albumentations as albu
import numpy as np
import os
import pandas as pd
import random
import torch
import warnings
import os
import cv2
from PIL import Image
import time
import copy
import warnings
import random
import numpy as np
import pandas as pd
from tqdm import tqdm_notebook as tqdm
from torch.optim.lr_scheduler import ReduceLROnPlateau
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.nn import functional as F
import torchvision
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader, Dataset, sampler
from matplotlib import pyplot as plt
import torchvision.transforms as transforms
from albumentations import HorizontalFlip, VerticalFlip, ShiftScaleRotate, Normalize, Resize, Compose, GaussNoise, RandomRotate90, Transpose, RandomBrightnessContrast, RandomCrop
from albumentations.pytorch import ToTensor
import albumentations as albu
import matplotlib.image as mpi
from sklearn.metrics import f1_score
warnings.filterwarnings('ignore')
seed = 69
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
labels = pd.read_csv('/kaggle/input/oxford-iiit/labels')
labels.shape
df1 = labels['breed']
df2 = labels['label_id']
df1 = pd.get_dummies(df1)
df = pd.concat([df2, df1], axis=1)
class DogandCat(Dataset):
def __init__(self, df, phase):
self.phase = phase
self.df = df
if phase == 'train':
self.transforms = albu.Compose([albu.SmallestMaxSize(256), albu.RandomCrop(256, 256), albu.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), ToTensor()])
elif phase == 'val':
self.transforms = albu.Compose([albu.Resize(256, 256), albu.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), ToTensor()])
def __len__(self):
return len(self.df)
def __getitem__(self, index):
label = self.df.iloc[index, 1:]
label = label.to_numpy()
label = np.argmax(label)
path = self.df.iloc[index, 0]
img = plt.imread(path)
img = self.transforms(image=np.array(img))
img = img['image']
return (img, label)
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
def im_show(img):
npimg = img.numpy().transpose((1, 2, 0)) * std + mean
npimg = np.clip(npimg, 0.0, 1.0)
plt.imshow(npimg)
fig = plt.figure(figsize=(18, 5))
for i in np.arange(16):
ax = fig.add_subplot(2, 8, i + 1, xticks=[], yticks=[])
im_show(img[i])
|
code
|
32065763/cell_9
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: 'replacement'})
id_stopword_dict = pd.read_csv('../input/indonesian-stoplist/stopwordbahasa.csv', header=None)
id_stopword_dict = id_stopword_dict.rename(columns={0: 'stopword'})
data.HS.value_counts()
data.Abusive.value_counts()
|
code
|
32065763/cell_2
|
[
"text_plain_output_1.png"
] |
!pip install PySastrawi
|
code
|
32065763/cell_19
|
[
"text_plain_output_1.png"
] |
import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: 'replacement'})
id_stopword_dict = pd.read_csv('../input/indonesian-stoplist/stopwordbahasa.csv', header=None)
id_stopword_dict = id_stopword_dict.rename(columns={0: 'stopword'})
data.HS.value_counts()
data.Abusive.value_counts()
print('Shape: ', data.shape)
data.head(15)
|
code
|
32065763/cell_7
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: 'replacement'})
id_stopword_dict = pd.read_csv('../input/indonesian-stoplist/stopwordbahasa.csv', header=None)
id_stopword_dict = id_stopword_dict.rename(columns={0: 'stopword'})
print('Shape: ', data.shape)
data.head(15)
|
code
|
32065763/cell_8
|
[
"text_plain_output_1.png"
] |
import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: 'replacement'})
id_stopword_dict = pd.read_csv('../input/indonesian-stoplist/stopwordbahasa.csv', header=None)
id_stopword_dict = id_stopword_dict.rename(columns={0: 'stopword'})
data.HS.value_counts()
|
code
|
32065763/cell_16
|
[
"text_plain_output_1.png"
] |
from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
import pandas as pd
import re
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: 'replacement'})
id_stopword_dict = pd.read_csv('../input/indonesian-stoplist/stopwordbahasa.csv', header=None)
id_stopword_dict = id_stopword_dict.rename(columns={0: 'stopword'})
import re
from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
factory = StemmerFactory()
stemmer = factory.create_stemmer()
def lowercase(text):
return text.lower()
def remove_unnecessary_char(text):
text = re.sub('\n', ' ', text)
text = re.sub('rt', ' ', text)
text = re.sub('user', ' ', text)
text = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+)|(http?://[^\\s]+))', ' ', text)
text = re.sub(' +', ' ', text)
return text
def remove_nonaplhanumeric(text):
text = re.sub('[^0-9a-zA-Z]+', ' ', text)
return text
alay_dict_map = dict(zip(alay_dict['original'], alay_dict['replacement']))
def normalize_alay(text):
return ' '.join([alay_dict_map[word] if word in alay_dict_map else word for word in text.split(' ')])
def remove_stopword(text):
text = ' '.join(['' if word in id_stopword_dict.stopword.values else word for word in text.split(' ')])
text = re.sub(' +', ' ', text)
text = text.strip()
return text
def stemming(text):
return stemmer.stem(text)
print('remove_nonaplhanumeric: ', remove_nonaplhanumeric('Halooo,,,,, duniaa!!'))
print('lowercase: ', lowercase('Halooo, duniaa!'))
print('stemming: ', stemming('Perekonomian Indonesia sedang dalam pertumbuhan yang membanggakan'))
print('remove_unnecessary_char: ', remove_unnecessary_char('Hehe\n\n RT USER USER apa kabs www.google.com\n hehe'))
print('normalize_alay: ', normalize_alay('aamiin adek abis'))
print('remove_stopword: ', remove_stopword('ada hehe adalah huhu yang hehe'))
|
code
|
32065763/cell_3
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
!ls '../input'
|
code
|
32065763/cell_14
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: 'replacement'})
id_stopword_dict = pd.read_csv('../input/indonesian-stoplist/stopwordbahasa.csv', header=None)
id_stopword_dict = id_stopword_dict.rename(columns={0: 'stopword'})
print('Shape: ', id_stopword_dict.shape)
id_stopword_dict.head()
|
code
|
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