path
stringlengths 13
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sequencelengths 1
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stringlengths 0
40.4k
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stringclasses 1
value |
|---|---|---|---|
2033003/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
import seaborn as sns
plt.close()
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = haberman['age']
y = haberman['operation_year']
z = haberman['axil_nodes']
ax.scatter(x, y, z, marker='o', c='r')
ax.set_xlabel('age')
ax.set_ylabel('operation_year')
ax.set_zlabel('')
plt.show() | code |
2033003/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
import seaborn as sns
plt.close()
#3D scattered plot.
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig=plt.figure()
ax=fig.add_subplot(111, projection='3d')
x=haberman["age"]
y=haberman["operation_year"]
z=haberman["axil_nodes"]
ax.scatter(x,y,z,marker='o', c='r');
ax.set_xlabel('age')
ax.set_ylabel('operation_year')
ax.set_zlabel('')
plt.show()
import numpy as np
plt.plot(haberman['axil_nodes'], np.zeros_like(haberman['axil_nodes']), 'o')
plt.show() | code |
2033003/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
haberman = pd.read_csv('../input/haberman.csv')
print(haberman.shape) | code |
2033003/cell_14 | [
"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)
import seaborn as sns
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
import seaborn as sns
plt.close()
sns.pairplot(haberman, hue='status')
plt.show() | code |
2033003/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
haberman['status'].value_counts() | code |
2033003/cell_12 | [
"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)
haberman = pd.read_csv('../input/haberman.csv')
import matplotlib.pyplot as plt
haberman.plot(kind='scatter', x='age', y='axil_nodes')
plt.show() | code |
2033003/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
haberman = pd.read_csv('../input/haberman.csv')
haberman.head(5) | code |
72084578/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import pandas as pd
import random
import tensorflow as tf
import tensorflow_hub as hub
train_dir = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_dir = '../input/yoga-poses-dataset/DATASET/TEST'
def plot_yoga_images(train_dir):
for i, col in enumerate(os.listdir(train_dir)):
image = random.choice(os.listdir(train_dir + '/' + col))
image_path = train_dir + '/' + col + '/' + image
img = mpimg.imread(image_path) / 255
plt.axis(False)
i = i + 1
from tensorflow.keras.preprocessing.image import ImageDataGenerator
IMAGE_SHAPE = (224, 224)
BATCH_SIZE = 32
train_datagen = ImageDataGenerator(rescale=1 / 255.0)
test_datagen = ImageDataGenerator(rescale=1 / 255.0)
train_data = train_datagen.flow_from_directory(train_dir, target_size=IMAGE_SHAPE, batch_size=BATCH_SIZE, class_mode='categorical')
test_data = test_datagen.flow_from_directory(test_dir, target_size=IMAGE_SHAPE, batch_size=BATCH_SIZE, class_mode='categorical')
import tensorflow_hub as hub
from tensorflow.keras import layers
efficientnet_url = 'https://tfhub.dev/tensorflow/efficientnet/b0/feature-vector/1'
resnet_url = 'https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5'
def create_model(model_url, num_classes=5):
feature_extractor_layer = hub.KerasLayer(model_url, trainable=False, name='feature_extraction_layer', input_shape=IMAGE_SHAPE + (3,))
model = tf.keras.Sequential([feature_extractor_layer, layers.Dense(num_classes, activation='softmax', name='output_layer')])
return model
def plot_loss(history):
model_df = pd.DataFrame(history.history)
loss = model_df.loss
val_loss = model_df.val_loss
accuracy = model_df.accuracy
val_accuracy = model_df.val_accuracy
epochs = range(len(model_df.loss))
resnet_model = create_model(resnet_url, num_classes=train_data.num_classes)
resnet_model.compile(loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
resnet_history = resnet_model.fit(train_data, epochs=5, steps_per_epoch=len(train_data), validation_data=test_data, validation_steps=len(test_data))
plot_loss(resnet_history) | code |
72084578/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_dir = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_dir = '../input/yoga-poses-dataset/DATASET/TEST'
from tensorflow.keras.preprocessing.image import ImageDataGenerator
IMAGE_SHAPE = (224, 224)
BATCH_SIZE = 32
train_datagen = ImageDataGenerator(rescale=1 / 255.0)
test_datagen = ImageDataGenerator(rescale=1 / 255.0)
print('Training Images')
train_data = train_datagen.flow_from_directory(train_dir, target_size=IMAGE_SHAPE, batch_size=BATCH_SIZE, class_mode='categorical')
print('Testing Images')
test_data = test_datagen.flow_from_directory(test_dir, target_size=IMAGE_SHAPE, batch_size=BATCH_SIZE, class_mode='categorical') | code |
72084578/cell_15 | [
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import pandas as pd
import random
import tensorflow as tf
import tensorflow_hub as hub
train_dir = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_dir = '../input/yoga-poses-dataset/DATASET/TEST'
def plot_yoga_images(train_dir):
for i, col in enumerate(os.listdir(train_dir)):
image = random.choice(os.listdir(train_dir + '/' + col))
image_path = train_dir + '/' + col + '/' + image
img = mpimg.imread(image_path) / 255
plt.axis(False)
i = i + 1
from tensorflow.keras.preprocessing.image import ImageDataGenerator
IMAGE_SHAPE = (224, 224)
BATCH_SIZE = 32
train_datagen = ImageDataGenerator(rescale=1 / 255.0)
test_datagen = ImageDataGenerator(rescale=1 / 255.0)
train_data = train_datagen.flow_from_directory(train_dir, target_size=IMAGE_SHAPE, batch_size=BATCH_SIZE, class_mode='categorical')
test_data = test_datagen.flow_from_directory(test_dir, target_size=IMAGE_SHAPE, batch_size=BATCH_SIZE, class_mode='categorical')
import tensorflow_hub as hub
from tensorflow.keras import layers
efficientnet_url = 'https://tfhub.dev/tensorflow/efficientnet/b0/feature-vector/1'
resnet_url = 'https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5'
def create_model(model_url, num_classes=5):
feature_extractor_layer = hub.KerasLayer(model_url, trainable=False, name='feature_extraction_layer', input_shape=IMAGE_SHAPE + (3,))
model = tf.keras.Sequential([feature_extractor_layer, layers.Dense(num_classes, activation='softmax', name='output_layer')])
return model
def plot_loss(history):
model_df = pd.DataFrame(history.history)
loss = model_df.loss
val_loss = model_df.val_loss
accuracy = model_df.accuracy
val_accuracy = model_df.val_accuracy
epochs = range(len(model_df.loss))
resnet_model = create_model(resnet_url, num_classes=train_data.num_classes)
resnet_model.compile(loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
resnet_history = resnet_model.fit(train_data, epochs=5, steps_per_epoch=len(train_data), validation_data=test_data, validation_steps=len(test_data))
efficientnet_model = create_model(efficientnet_url, num_classes=train_data.num_classes)
efficientnet_model.compile(loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
efficientnet_history = efficientnet_model.fit(train_data, epochs=5, steps_per_epoch=len(train_data), validation_data=test_data, validation_steps=len(test_data))
plot_loss(efficientnet_history) | code |
72084578/cell_14 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
import tensorflow_hub as hub
train_dir = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_dir = '../input/yoga-poses-dataset/DATASET/TEST'
from tensorflow.keras.preprocessing.image import ImageDataGenerator
IMAGE_SHAPE = (224, 224)
BATCH_SIZE = 32
train_datagen = ImageDataGenerator(rescale=1 / 255.0)
test_datagen = ImageDataGenerator(rescale=1 / 255.0)
train_data = train_datagen.flow_from_directory(train_dir, target_size=IMAGE_SHAPE, batch_size=BATCH_SIZE, class_mode='categorical')
test_data = test_datagen.flow_from_directory(test_dir, target_size=IMAGE_SHAPE, batch_size=BATCH_SIZE, class_mode='categorical')
import tensorflow_hub as hub
from tensorflow.keras import layers
efficientnet_url = 'https://tfhub.dev/tensorflow/efficientnet/b0/feature-vector/1'
resnet_url = 'https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5'
def create_model(model_url, num_classes=5):
feature_extractor_layer = hub.KerasLayer(model_url, trainable=False, name='feature_extraction_layer', input_shape=IMAGE_SHAPE + (3,))
model = tf.keras.Sequential([feature_extractor_layer, layers.Dense(num_classes, activation='softmax', name='output_layer')])
return model
resnet_model = create_model(resnet_url, num_classes=train_data.num_classes)
resnet_model.compile(loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
resnet_history = resnet_model.fit(train_data, epochs=5, steps_per_epoch=len(train_data), validation_data=test_data, validation_steps=len(test_data))
efficientnet_model = create_model(efficientnet_url, num_classes=train_data.num_classes)
efficientnet_model.compile(loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
efficientnet_history = efficientnet_model.fit(train_data, epochs=5, steps_per_epoch=len(train_data), validation_data=test_data, validation_steps=len(test_data)) | code |
72084578/cell_12 | [
"image_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
import tensorflow_hub as hub
train_dir = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_dir = '../input/yoga-poses-dataset/DATASET/TEST'
from tensorflow.keras.preprocessing.image import ImageDataGenerator
IMAGE_SHAPE = (224, 224)
BATCH_SIZE = 32
train_datagen = ImageDataGenerator(rescale=1 / 255.0)
test_datagen = ImageDataGenerator(rescale=1 / 255.0)
train_data = train_datagen.flow_from_directory(train_dir, target_size=IMAGE_SHAPE, batch_size=BATCH_SIZE, class_mode='categorical')
test_data = test_datagen.flow_from_directory(test_dir, target_size=IMAGE_SHAPE, batch_size=BATCH_SIZE, class_mode='categorical')
import tensorflow_hub as hub
from tensorflow.keras import layers
efficientnet_url = 'https://tfhub.dev/tensorflow/efficientnet/b0/feature-vector/1'
resnet_url = 'https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5'
def create_model(model_url, num_classes=5):
feature_extractor_layer = hub.KerasLayer(model_url, trainable=False, name='feature_extraction_layer', input_shape=IMAGE_SHAPE + (3,))
model = tf.keras.Sequential([feature_extractor_layer, layers.Dense(num_classes, activation='softmax', name='output_layer')])
return model
resnet_model = create_model(resnet_url, num_classes=train_data.num_classes)
resnet_model.compile(loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
resnet_history = resnet_model.fit(train_data, epochs=5, steps_per_epoch=len(train_data), validation_data=test_data, validation_steps=len(test_data)) | code |
72084578/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import random
train_dir = '../input/yoga-poses-dataset/DATASET/TRAIN'
test_dir = '../input/yoga-poses-dataset/DATASET/TEST'
def plot_yoga_images(train_dir):
for i, col in enumerate(os.listdir(train_dir)):
image = random.choice(os.listdir(train_dir + '/' + col))
image_path = train_dir + '/' + col + '/' + image
img = mpimg.imread(image_path) / 255
plt.axis(False)
i = i + 1
plot_yoga_images(train_dir) | code |
128037854/cell_4 | [
"text_plain_output_1.png"
] | # 开始训练模型前50轮
!python /kaggle/working/yolov5-6-1ming/train.py --img 544 --batch 16 --epochs 50 --data /kaggle/working/widerpersonming/WiderPerson/person.yaml --cfg /kaggle/working/widerpersonming/yolov5s_SE.yaml | code |
128037854/cell_6 | [
"text_plain_output_1.png"
] | import datetime
import os
import os
import os
import zipfile
import os
import zipfile
import datetime
def file2zip(packagePath, zipPath):
"""
:param packagePath: 文件夹路径
:param zipPath: 压缩包路径
:return:
"""
zip = zipfile.ZipFile(zipPath, 'w', zipfile.ZIP_DEFLATED)
for path, dirNames, fileNames in os.walk(packagePath):
fpath = path.replace(packagePath, '')
for name in fileNames:
fullName = os.path.join(path, name)
name = fpath + '\\' + name
zip.write(fullName, name)
zip.close()
if __name__ == '__main__':
packagePath = '/kaggle/working/yolov5-6-1ming/runs/train/exp2'
zipPath = '/kaggle/working/yolov5s_SE-50-544-simplified-one-layer-only-pedestrians.zip'
if os.path.exists(zipPath):
os.remove(zipPath)
file2zip(packagePath, zipPath)
print('打包完成')
print(datetime.datetime.utcnow()) | code |
128037854/cell_2 | [
"text_plain_output_1.png"
] | import os
filepath = '/kaggle/working/widerpersonming/WiderPerson/person.yaml'
datas = []
datas.append('train: /kaggle/working/widerpersonming/WiderPerson/train/images')
datas.append('\n')
datas.append('val: /kaggle/working/widerpersonming/WiderPerson/val/images')
datas.append('\n')
datas.append('nc: 1')
datas.append('\n')
datas.append("names: ['pedestrians']")
datas.append('\n')
print(datas)
with open(filepath, 'w') as f:
f.writelines(datas) | code |
128037854/cell_1 | [
"text_plain_output_1.png"
] | import shutil
import shutil
shutil.copytree('../input/yolov5-6-1ming-with-se', './yolov5-6-1ming')
shutil.copytree('../input/widerpersonming-only-pedestrians', './widerpersonming') | code |
128037854/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from IPython.display import FileLink
FileLink('yolov5s_SE-50-544-simplified-one-layer-only-pedestrians.zip') | code |
128037854/cell_3 | [
"text_plain_output_1.png"
] | import os
filepath = '/kaggle/working/widerpersonming/WiderPerson/person.yaml'
datas = []
datas.append('train: /kaggle/working/widerpersonming/WiderPerson/train/images')
datas.append('\n')
datas.append('val: /kaggle/working/widerpersonming/WiderPerson/val/images')
datas.append('\n')
datas.append('nc: 1')
datas.append('\n')
datas.append("names: ['pedestrians']")
datas.append('\n')
with open(filepath, 'w') as f:
f.writelines(datas)
import os
filepath = '/kaggle/working/widerpersonming/WiderPerson/person.yaml'
with open(filepath, 'r') as f:
datas = f.readlines()
print(datas)
print(type(datas)) | code |
128025412/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
display(df_iris.head(3))
display(df_iris.tail(3))
display(df_iris.describe()) | code |
128025412/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size() | code |
128025412/cell_26 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
le = LabelEncoder()
y['Species'] = le.fit_transform(y['Species'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
new_data = pd.DataFrame([[5.1, 3.5, 1.4, 0.2], [6.2, 2.8, 4.8, 1.8], [7.3, 3.0, 6.3, 2.5]])
new_data.columns = X.columns
display(new_data) | code |
128025412/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
from pandas.plotting import andrews_curves
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
from sklearn.metrics import RocCurveDisplay, classification_report
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns | code |
128025412/cell_11 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | from pandas.plotting import andrews_curves
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
le = LabelEncoder()
y['Species'] = le.fit_transform(y['Species'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
andrews_curves(df_iris.drop('Id', axis=1), 'Species')
plt.figure()
sns.pairplot(df_iris.drop('Id', axis=1), hue='Species', height=3, markers=['o', 's', 'D'])
plt.show() | code |
128025412/cell_19 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import RocCurveDisplay, classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
le = LabelEncoder()
y['Species'] = le.fit_transform(y['Species'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
model = Pipeline([('scaler', StandardScaler()), ('classifier', LogisticRegression())])
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(classification_report(y_pred, y_test, target_names=list(le.classes_))) | code |
128025412/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
display(X.head(3), y.head(3))
le = LabelEncoder()
y['Species'] = le.fit_transform(y['Species'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
display(X_train.describe(), y_test.describe()) | code |
128025412/cell_15 | [
"text_html_output_4.png",
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
le = LabelEncoder()
y['Species'] = le.fit_transform(y['Species'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
model = Pipeline([('scaler', StandardScaler()), ('classifier', LogisticRegression())])
model.fit(X_train, y_train) | code |
128025412/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas.plotting import andrews_curves
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import RocCurveDisplay, classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
le = LabelEncoder()
y['Species'] = le.fit_transform(y['Species'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
andrews_curves(df_iris.drop('Id', axis=1), 'Species')
df_iris.drop('Id', axis=1).boxplot(by='Species', figsize=(15, 10))
model = Pipeline([('scaler', StandardScaler()), ('classifier', LogisticRegression())])
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_score = model.predict_proba(X_test)
def show_roc(class_of_interest):
label_binarizer = LabelBinarizer().fit(y_train)
y_onehot_test = label_binarizer.transform(y_test)
label_binarizer.transform([class_of_interest])
class_id = np.flatnonzero(label_binarizer.classes_ == class_of_interest)[0]
RocCurveDisplay.from_predictions(y_onehot_test[:, class_id], y_score[:, class_id], name=f'{class_of_interest} vs the rest')
plt.axis('square')
show_roc('Iris-setosa')
show_roc('Iris-versicolor')
show_roc('Iris-virginica') | code |
128025412/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pandas.plotting import andrews_curves
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import matplotlib.pyplot as plt
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
le = LabelEncoder()
y['Species'] = le.fit_transform(y['Species'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
plt.figure(figsize=(15, 10))
andrews_curves(df_iris.drop('Id', axis=1), 'Species')
plt.title('Andrews Curves Plot', fontsize=20, fontweight='bold')
plt.legend(loc=1, prop={'size': 15}, frameon=True, facecolor='white', edgecolor='black')
plt.show() | code |
128025412/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import RocCurveDisplay, classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import pandas as pd
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
le = LabelEncoder()
y['Species'] = le.fit_transform(y['Species'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
model = Pipeline([('scaler', StandardScaler()), ('classifier', LogisticRegression())])
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_train['Species'] = le.inverse_transform(y_train['Species'])
y_test['Species'] = le.inverse_transform(y_test['Species'])
y_score = model.predict_proba(X_test)
new_data = pd.DataFrame([[5.1, 3.5, 1.4, 0.2], [6.2, 2.8, 4.8, 1.8], [7.3, 3.0, 6.3, 2.5]])
new_data.columns = X.columns
predictions = model.predict(new_data.values)
print(le.inverse_transform(predictions)) | code |
128025412/cell_12 | [
"text_plain_output_1.png"
] | from pandas.plotting import andrews_curves
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder, LabelBinarizer
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_iris = pd.DataFrame(pd.read_csv('/kaggle/input/iris/Iris.csv'))
df_iris.groupby('Species').size()
X = df_iris.iloc[:, 1:5]
y = pd.DataFrame(df_iris.iloc[:, 5])
le = LabelEncoder()
y['Species'] = le.fit_transform(y['Species'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
andrews_curves(df_iris.drop('Id', axis=1), 'Species')
plt.figure()
df_iris.drop('Id', axis=1).boxplot(by='Species', figsize=(15, 10))
plt.show() | code |
50242767/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(x, y)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=3)
x_poly = poly_reg.fit_transform(x)
lin_reg2 = LinearRegression()
lin_reg2.fit(x_poly, y)
x_grid = np.arange(min(x), max(x), 0.1)
x_grid = x_grid.reshape((len(x_grid), 1))
plt.scatter(x, y, color='red')
plt.plot(x, lin_reg2.predict(x_poly), color='blue')
plt.xlabel('Age')
plt.ylabel('Height')
plt.title('Polynomial Linear Regression ') | code |
50242767/cell_9 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(x, y) | code |
50242767/cell_25 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
data[data['Age'] == 30] | code |
50242767/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv') | code |
50242767/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(x, y)
plt.scatter(x, y, color='red')
plt.plot(x, lin_reg.predict(x), color='blue')
plt.xlabel('Age')
plt.ylabel('Height')
plt.title('Linear Regression for Height and Weight') | code |
50242767/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=3)
x_poly = poly_reg.fit_transform(x)
lin_reg2 = LinearRegression()
lin_reg2.fit(x_poly, y) | code |
50242767/cell_15 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(x, y)
lin_reg.predict([[30]]) | code |
50242767/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
data.head(4) | code |
50242767/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
x = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(x, y)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=3)
x_poly = poly_reg.fit_transform(x)
lin_reg2 = LinearRegression()
lin_reg2.fit(x_poly, y)
x_grid = np.arange(min(x), max(x), 0.1)
x_grid = x_grid.reshape((len(x_grid), 1))
lin_reg2.predict(poly_reg.fit_transform([[30]])) | code |
50242767/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/heightvsweight-for-linear-polynomial-regression/HeightVsWeight.csv')
sns.scatterplot(x='Age', y='Height', data=data) | code |
88090787/cell_42 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape) | code |
88090787/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.describe() | code |
88090787/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.info() | code |
88090787/cell_4 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns | code |
88090787/cell_56 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]
for d in data:
d['Title'] = d['Title'].replace(['Capt', 'Col', 'Countess', 'Don', 'Dr', 'Jonkheer', 'Lady', 'Major', 'Sir', 'Rev'], 'Rare')
d['Title'] = d['Title'].replace(['Mlle', 'Mme'], 'Miss')
d['Title'] = d['Title'].replace('Mme', 'Mrs')
train[['Title', 'Survived']].groupby('Title').mean()
titles = {'Master': 1, 'Miss': 2, 'Mr': 3, 'Mrs': 4, 'Ms': 5, 'Rare': 6}
for d in data:
d['Title'] = d['Title'].map(titles)
d['Title'] = d['Title'].fillna(0)
train = train.drop(['PassengerId', 'Name'], axis=1)
test = test.drop(['PassengerId', 'Name'], axis=1)
data = [train, test]
(train.shape, test.shape)
for i in data:
i['Sex'] = i['Sex'].map({'male': 0, 'female': 1}).astype(int)
ages = np.zeros((2, 3))
ages
for d in data:
for i in range(0, 2):
for j in range(0, 3):
age_data = d[(d['Sex'] == i) & (d['Pclass'] == j + 1)]['Age'].dropna()
age = age_data.median()
ages[i, j] = age
for i in range(0, 2):
for j in range(0, 3):
d.loc[d['Age'].isnull() & (d['Sex'] == i) & (d['Pclass'] == j + 1), 'Age'] = ages[i, j]
d['Age'] = d['Age'].astype(int)
for d in data:
d.loc[d.Age <= 16, 'Age'] = 0
d.loc[(d.Age > 16) & (d.Age <= 32), 'Age'] = 1
d.loc[(d.Age > 32) & (d.Age <= 48), 'Age'] = 2
d.loc[(d.Age > 48) & (d.Age <= 64), 'Age'] = 3
d.loc[d.Age > 64, 'Age'] = 4
train = train.drop('SegAge', axis=1)
data = [train, test]
train.head() | code |
88090787/cell_33 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
g = sns.FacetGrid(data=train, col='Survived')
g = g.map(plt.hist, 'Age', bins=25)
grid = sns.FacetGrid(data=train, col='Survived', row='Pclass', size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=0.7, bins=20)
grid.add_legend() | code |
88090787/cell_44 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
pd.crosstab(train['Title'], train['Sex']) | code |
88090787/cell_55 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]
for d in data:
d['Title'] = d['Title'].replace(['Capt', 'Col', 'Countess', 'Don', 'Dr', 'Jonkheer', 'Lady', 'Major', 'Sir', 'Rev'], 'Rare')
d['Title'] = d['Title'].replace(['Mlle', 'Mme'], 'Miss')
d['Title'] = d['Title'].replace('Mme', 'Mrs')
train[['Title', 'Survived']].groupby('Title').mean()
titles = {'Master': 1, 'Miss': 2, 'Mr': 3, 'Mrs': 4, 'Ms': 5, 'Rare': 6}
for d in data:
d['Title'] = d['Title'].map(titles)
d['Title'] = d['Title'].fillna(0)
train = train.drop(['PassengerId', 'Name'], axis=1)
test = test.drop(['PassengerId', 'Name'], axis=1)
data = [train, test]
(train.shape, test.shape)
for i in data:
i['Sex'] = i['Sex'].map({'male': 0, 'female': 1}).astype(int)
ages = np.zeros((2, 3))
ages
for d in data:
for i in range(0, 2):
for j in range(0, 3):
age_data = d[(d['Sex'] == i) & (d['Pclass'] == j + 1)]['Age'].dropna()
age = age_data.median()
ages[i, j] = age
for i in range(0, 2):
for j in range(0, 3):
d.loc[d['Age'].isnull() & (d['Sex'] == i) & (d['Pclass'] == j + 1), 'Age'] = ages[i, j]
d['Age'] = d['Age'].astype(int)
for d in data:
d.loc[d.Age <= 16, 'Age'] = 0
d.loc[(d.Age > 16) & (d.Age <= 32), 'Age'] = 1
d.loc[(d.Age > 32) & (d.Age <= 48), 'Age'] = 2
d.loc[(d.Age > 48) & (d.Age <= 64), 'Age'] = 3
d.loc[d.Age > 64, 'Age'] = 4
train.head() | code |
88090787/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.head() | code |
88090787/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train[['Parch', 'Survived']].groupby('Parch', as_index=False).mean().sort_values(by='Parch', ascending=True) | code |
88090787/cell_39 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
g = sns.FacetGrid(data=train, col='Survived')
g = g.map(plt.hist, 'Age', bins=25)
grid = sns.FacetGrid(data=train, col='Survived', row='Pclass', size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=0.7, bins=20)
grid.add_legend()
g = sns.FacetGrid(data=train, row='Embarked', size=2.2, aspect=1.6)
g.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')
g.add_legend()
g = sns.FacetGrid(data=train, col='Survived', row='Embarked', size=2.2, aspect=2.2)
g.map(sns.barplot, 'Sex', 'Fare')
g.add_legend() | code |
88090787/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train[['Sex', 'Survived']].groupby('Sex', as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
88090787/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]
for d in data:
d['Title'] = d['Title'].replace(['Capt', 'Col', 'Countess', 'Don', 'Dr', 'Jonkheer', 'Lady', 'Major', 'Sir', 'Rev'], 'Rare')
d['Title'] = d['Title'].replace(['Mlle', 'Mme'], 'Miss')
d['Title'] = d['Title'].replace('Mme', 'Mrs')
train[['Title', 'Survived']].groupby('Title').mean()
titles = {'Master': 1, 'Miss': 2, 'Mr': 3, 'Mrs': 4, 'Ms': 5, 'Rare': 6}
for d in data:
d['Title'] = d['Title'].map(titles)
d['Title'] = d['Title'].fillna(0)
train = train.drop(['PassengerId', 'Name'], axis=1)
test = test.drop(['PassengerId', 'Name'], axis=1)
data = [train, test]
(train.shape, test.shape) | code |
88090787/cell_54 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
pd.crosstab(train['Title'], train['Sex'])
data = [train, test]
for d in data:
d['Title'] = d['Title'].replace(['Capt', 'Col', 'Countess', 'Don', 'Dr', 'Jonkheer', 'Lady', 'Major', 'Sir', 'Rev'], 'Rare')
d['Title'] = d['Title'].replace(['Mlle', 'Mme'], 'Miss')
d['Title'] = d['Title'].replace('Mme', 'Mrs')
train[['Title', 'Survived']].groupby('Title').mean()
titles = {'Master': 1, 'Miss': 2, 'Mr': 3, 'Mrs': 4, 'Ms': 5, 'Rare': 6}
for d in data:
d['Title'] = d['Title'].map(titles)
d['Title'] = d['Title'].fillna(0)
train = train.drop(['PassengerId', 'Name'], axis=1)
test = test.drop(['PassengerId', 'Name'], axis=1)
data = [train, test]
(train.shape, test.shape)
for i in data:
i['Sex'] = i['Sex'].map({'male': 0, 'female': 1}).astype(int)
ages = np.zeros((2, 3))
ages
for d in data:
for i in range(0, 2):
for j in range(0, 3):
age_data = d[(d['Sex'] == i) & (d['Pclass'] == j + 1)]['Age'].dropna()
age = age_data.median()
ages[i, j] = age
for i in range(0, 2):
for j in range(0, 3):
d.loc[d['Age'].isnull() & (d['Sex'] == i) & (d['Pclass'] == j + 1), 'Age'] = ages[i, j]
d['Age'] = d['Age'].astype(int)
train['SegAge'] = pd.cut(train['Age'], 5)
train[['SegAge', 'Survived']].groupby('SegAge', as_index=False).mean() | code |
88090787/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.info() | code |
88090787/cell_50 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
g = sns.FacetGrid(data=train, col='Survived')
g = g.map(plt.hist, 'Age', bins=25)
grid = sns.FacetGrid(data=train, col='Survived', row='Pclass', size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=0.7, bins=20)
grid.add_legend()
g = sns.FacetGrid(data=train, row='Embarked', size=2.2, aspect=1.6)
g.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')
g.add_legend()
g = sns.FacetGrid(data=train, col='Survived', row='Embarked', size=2.2, aspect=2.2)
g.map(sns.barplot, 'Sex', 'Fare')
g.add_legend()
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]
for d in data:
d['Title'] = d['Title'].replace(['Capt', 'Col', 'Countess', 'Don', 'Dr', 'Jonkheer', 'Lady', 'Major', 'Sir', 'Rev'], 'Rare')
d['Title'] = d['Title'].replace(['Mlle', 'Mme'], 'Miss')
d['Title'] = d['Title'].replace('Mme', 'Mrs')
train[['Title', 'Survived']].groupby('Title').mean()
titles = {'Master': 1, 'Miss': 2, 'Mr': 3, 'Mrs': 4, 'Ms': 5, 'Rare': 6}
for d in data:
d['Title'] = d['Title'].map(titles)
d['Title'] = d['Title'].fillna(0)
train = train.drop(['PassengerId', 'Name'], axis=1)
test = test.drop(['PassengerId', 'Name'], axis=1)
data = [train, test]
(train.shape, test.shape)
for i in data:
i['Sex'] = i['Sex'].map({'male': 0, 'female': 1}).astype(int)
g = sns.FacetGrid(train, col='Sex', row='Pclass', size=2.2, aspect=1.6)
g.map(plt.hist, 'Age', alpha=0.7, bins=20)
g.add_legend() | code |
88090787/cell_52 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]
for d in data:
d['Title'] = d['Title'].replace(['Capt', 'Col', 'Countess', 'Don', 'Dr', 'Jonkheer', 'Lady', 'Major', 'Sir', 'Rev'], 'Rare')
d['Title'] = d['Title'].replace(['Mlle', 'Mme'], 'Miss')
d['Title'] = d['Title'].replace('Mme', 'Mrs')
train[['Title', 'Survived']].groupby('Title').mean()
titles = {'Master': 1, 'Miss': 2, 'Mr': 3, 'Mrs': 4, 'Ms': 5, 'Rare': 6}
for d in data:
d['Title'] = d['Title'].map(titles)
d['Title'] = d['Title'].fillna(0)
train = train.drop(['PassengerId', 'Name'], axis=1)
test = test.drop(['PassengerId', 'Name'], axis=1)
data = [train, test]
(train.shape, test.shape)
for i in data:
i['Sex'] = i['Sex'].map({'male': 0, 'female': 1}).astype(int)
ages = np.zeros((2, 3))
ages
for d in data:
for i in range(0, 2):
for j in range(0, 3):
age_data = d[(d['Sex'] == i) & (d['Pclass'] == j + 1)]['Age'].dropna()
age = age_data.median()
ages[i, j] = age
for i in range(0, 2):
for j in range(0, 3):
d.loc[d['Age'].isnull() & (d['Sex'] == i) & (d['Pclass'] == j + 1), 'Age'] = ages[i, j]
d['Age'] = d['Age'].astype(int)
train.head() | code |
88090787/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]
for d in data:
d['Title'] = d['Title'].replace(['Capt', 'Col', 'Countess', 'Don', 'Dr', 'Jonkheer', 'Lady', 'Major', 'Sir', 'Rev'], 'Rare')
d['Title'] = d['Title'].replace(['Mlle', 'Mme'], 'Miss')
d['Title'] = d['Title'].replace('Mme', 'Mrs')
train[['Title', 'Survived']].groupby('Title').mean() | code |
88090787/cell_49 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]
for d in data:
d['Title'] = d['Title'].replace(['Capt', 'Col', 'Countess', 'Don', 'Dr', 'Jonkheer', 'Lady', 'Major', 'Sir', 'Rev'], 'Rare')
d['Title'] = d['Title'].replace(['Mlle', 'Mme'], 'Miss')
d['Title'] = d['Title'].replace('Mme', 'Mrs')
train[['Title', 'Survived']].groupby('Title').mean()
titles = {'Master': 1, 'Miss': 2, 'Mr': 3, 'Mrs': 4, 'Ms': 5, 'Rare': 6}
for d in data:
d['Title'] = d['Title'].map(titles)
d['Title'] = d['Title'].fillna(0)
train = train.drop(['PassengerId', 'Name'], axis=1)
test = test.drop(['PassengerId', 'Name'], axis=1)
data = [train, test]
(train.shape, test.shape)
for i in data:
i['Sex'] = i['Sex'].map({'male': 0, 'female': 1}).astype(int)
train.head(10) | code |
88090787/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.describe(include=['O']) | code |
88090787/cell_51 | [
"text_html_output_1.png"
] | import numpy as np
ages = np.zeros((2, 3))
ages | code |
88090787/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train[['SibSp', 'Survived']].groupby('SibSp', as_index=False).mean().sort_values(by='SibSp', ascending=True) | code |
88090787/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]
for d in data:
d['Title'] = d['Title'].replace(['Capt', 'Col', 'Countess', 'Don', 'Dr', 'Jonkheer', 'Lady', 'Major', 'Sir', 'Rev'], 'Rare')
d['Title'] = d['Title'].replace(['Mlle', 'Mme'], 'Miss')
d['Title'] = d['Title'].replace('Mme', 'Mrs')
train[['Title', 'Survived']].groupby('Title').mean()
titles = {'Master': 1, 'Miss': 2, 'Mr': 3, 'Mrs': 4, 'Ms': 5, 'Rare': 6}
for d in data:
d['Title'] = d['Title'].map(titles)
d['Title'] = d['Title'].fillna(0)
train.head() | code |
88090787/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape | code |
88090787/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
train['Title'] = train['Name'].str.extract('([A-Za-z]+)\\.')
test['Title'] = test['Name'].str.extract('([A-Za-z]+)\\.')
(train['Title'], test['Title']) | code |
88090787/cell_31 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
g = sns.FacetGrid(data=train, col='Survived')
g = g.map(plt.hist, 'Age', bins=25) | code |
88090787/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
88090787/cell_53 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data = pd.concat([train, test])
data.shape
train.columns
train = train.drop(['Cabin', 'Ticket'], axis=1)
test = test.drop(['Cabin', 'Ticket'], axis=1)
(train.shape, test.shape)
data = [train, test]
for d in data:
d['Title'] = d['Title'].replace(['Capt', 'Col', 'Countess', 'Don', 'Dr', 'Jonkheer', 'Lady', 'Major', 'Sir', 'Rev'], 'Rare')
d['Title'] = d['Title'].replace(['Mlle', 'Mme'], 'Miss')
d['Title'] = d['Title'].replace('Mme', 'Mrs')
train[['Title', 'Survived']].groupby('Title').mean()
titles = {'Master': 1, 'Miss': 2, 'Mr': 3, 'Mrs': 4, 'Ms': 5, 'Rare': 6}
for d in data:
d['Title'] = d['Title'].map(titles)
d['Title'] = d['Title'].fillna(0)
train = train.drop(['PassengerId', 'Name'], axis=1)
test = test.drop(['PassengerId', 'Name'], axis=1)
data = [train, test]
(train.shape, test.shape)
for i in data:
i['Sex'] = i['Sex'].map({'male': 0, 'female': 1}).astype(int)
ages = np.zeros((2, 3))
ages
for d in data:
for i in range(0, 2):
for j in range(0, 3):
age_data = d[(d['Sex'] == i) & (d['Pclass'] == j + 1)]['Age'].dropna()
age = age_data.median()
ages[i, j] = age
for i in range(0, 2):
for j in range(0, 3):
d.loc[d['Age'].isnull() & (d['Sex'] == i) & (d['Pclass'] == j + 1), 'Age'] = ages[i, j]
d['Age'] = d['Age'].astype(int)
(train['Age'].isnull().sum(), test['Age'].isnull().sum()) | code |
88090787/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
g = sns.FacetGrid(data=train, col='Survived')
g = g.map(plt.hist, 'Age', bins=25)
grid = sns.FacetGrid(data=train, col='Survived', row='Pclass', size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=0.7, bins=20)
grid.add_legend()
g = sns.FacetGrid(data=train, row='Embarked', size=2.2, aspect=1.6)
g.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')
g.add_legend() | code |
88090787/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.columns
train.head() | code |
1006521/cell_4 | [
"text_html_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode(connected=True)
terror_data = pd.read_csv('../input/globalterrorismdb_0616dist.csv', encoding='ISO-8859-1', usecols=[0, 1, 2, 3, 8, 11, 13, 14, 35, 84, 100, 103])
terror_data = terror_data.rename(columns={'eventid': 'id', 'iyear': 'year', 'imonth': 'month', 'iday': 'day', 'country_txt': 'country', 'provstate': 'state', 'targtype1_txt': 'target', 'weaptype1_txt': 'weapon', 'nkill': 'fatalities', 'nwound': 'injuries'})
terror_data['fatalities'] = terror_data['fatalities'].fillna(0).astype(int)
terror_data['injuries'] = terror_data['injuries'].fillna(0).astype(int)
attacks_france = terror_data[terror_data.country == 'France']
terror_peryear = np.asarray(attacks_france.groupby('year').year.count())
terror_years = np.arange(1972, 2016)
terror_years = np.delete(terror_years, [23])
trace = [go.Scatter(x=terror_years, y=terror_peryear, mode='lines', line=dict(color='rgb(240, 140, 45)', width=3))]
layout = go.Layout(title='Terrorist Attacks by Year in France (1970-2015)', xaxis=dict(rangeslider=dict(thickness=0.05), showline=True, showgrid=False), yaxis=dict(range=[0.1, 425], showline=True, showgrid=False))
figure = dict(data=trace, layout=layout)
iplot(figure) | code |
1006521/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode(connected=True)
terror_data = pd.read_csv('../input/globalterrorismdb_0616dist.csv', encoding='ISO-8859-1', usecols=[0, 1, 2, 3, 8, 11, 13, 14, 35, 84, 100, 103])
terror_data = terror_data.rename(columns={'eventid': 'id', 'iyear': 'year', 'imonth': 'month', 'iday': 'day', 'country_txt': 'country', 'provstate': 'state', 'targtype1_txt': 'target', 'weaptype1_txt': 'weapon', 'nkill': 'fatalities', 'nwound': 'injuries'})
terror_data['fatalities'] = terror_data['fatalities'].fillna(0).astype(int)
terror_data['injuries'] = terror_data['injuries'].fillna(0).astype(int)
attacks_france = terror_data[terror_data.country == 'France']
terror_peryear = np.asarray(attacks_france.groupby('year').year.count())
terror_years = np.arange(1972, 2016)
terror_years = np.delete(terror_years, [23])
trace = [go.Scatter(x=terror_years, y=terror_peryear, mode='lines', line=dict(color='rgb(240, 140, 45)', width=3))]
layout = go.Layout(title='Terrorist Attacks by Year in France (1970-2015)', xaxis=dict(rangeslider=dict(thickness=0.05), showline=True, showgrid=False), yaxis=dict(range=[0.1, 425], showline=True, showgrid=False))
figure = dict(data=trace, layout=layout)
attacks_france['text'] = attacks_france['date'].dt.strftime('%B %-d, %Y') + '<br>' + attacks_france['fatalities'].astype(str) + ' Killed, ' + attacks_france['injuries'].astype(str) + ' Injured'
fatality = dict(type='scattergeo', locationmode='USA-states', lon=attacks_france[attacks_france.fatalities > 0]['longitude'], lat=attacks_france[attacks_france.fatalities > 0]['latitude'], text=attacks_france[attacks_france.fatalities > 0]['text'], mode='markers', name='Fatalities', hoverinfo='text+name', marker=dict(size=attacks_france[attacks_france.fatalities > 0]['fatalities'] ** 0.255 * 8, opacity=0.95, color='rgb(240, 140, 45)'))
injury = dict(type='scattergeo', locationmode='USA-states', lon=attacks_france[attacks_france.fatalities == 0]['longitude'], lat=attacks_france[attacks_france.fatalities == 0]['latitude'], text=attacks_france[attacks_france.fatalities == 0]['text'], mode='markers', name='Injuries', hoverinfo='text+name', marker=dict(size=(attacks_france[attacks_france.fatalities == 0]['injuries'] + 1) ** 0.245 * 8, opacity=0.85, color='rgb(20, 150, 187)'))
layout = dict(title='Terrorist Attacks by Latitude/Longitude in France (1970-2015)', showlegend=True, legend=dict(x=0.85, y=0.4), geo=dict(scope='europe', projection=dict(type='albers usa'), showland=True, landcolor='rgb(250, 250, 250)', subunitwidth=1, subunitcolor='rgb(217, 217, 217)', countrywidth=1, countrycolor='rgb(217, 217, 217)', showlakes=True, lakecolor='rgb(255, 255, 255)'))
data = [fatality, injury]
figure = dict(data=data, layout=layout)
iplot(figure) | code |
1006521/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1006521/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode(connected=True)
terror_data = pd.read_csv('../input/globalterrorismdb_0616dist.csv', encoding='ISO-8859-1', usecols=[0, 1, 2, 3, 8, 11, 13, 14, 35, 84, 100, 103])
terror_data = terror_data.rename(columns={'eventid': 'id', 'iyear': 'year', 'imonth': 'month', 'iday': 'day', 'country_txt': 'country', 'provstate': 'state', 'targtype1_txt': 'target', 'weaptype1_txt': 'weapon', 'nkill': 'fatalities', 'nwound': 'injuries'})
terror_data['fatalities'] = terror_data['fatalities'].fillna(0).astype(int)
terror_data['injuries'] = terror_data['injuries'].fillna(0).astype(int)
attacks_france = terror_data[terror_data.country == 'France']
terror_peryear = np.asarray(attacks_france.groupby('year').year.count())
terror_years = np.arange(1972, 2016)
terror_years = np.delete(terror_years, [23])
trace = [go.Scatter(x=terror_years, y=terror_peryear, mode='lines', line=dict(color='rgb(240, 140, 45)', width=3))]
layout = go.Layout(title='Terrorist Attacks by Year in France (1970-2015)', xaxis=dict(rangeslider=dict(thickness=0.05), showline=True, showgrid=False), yaxis=dict(range=[0.1, 425], showline=True, showgrid=False))
figure = dict(data=trace, layout=layout)
attacks_france['text'] = attacks_france['date'].dt.strftime('%B %-d, %Y') + '<br>' + attacks_france['fatalities'].astype(str) + ' Killed, ' + attacks_france['injuries'].astype(str) + ' Injured'
fatality = dict(type='scattergeo', locationmode='USA-states', lon=attacks_france[attacks_france.fatalities > 0]['longitude'], lat=attacks_france[attacks_france.fatalities > 0]['latitude'], text=attacks_france[attacks_france.fatalities > 0]['text'], mode='markers', name='Fatalities', hoverinfo='text+name', marker=dict(size=attacks_france[attacks_france.fatalities > 0]['fatalities'] ** 0.255 * 8, opacity=0.95, color='rgb(240, 140, 45)'))
injury = dict(type='scattergeo', locationmode='USA-states', lon=attacks_france[attacks_france.fatalities == 0]['longitude'], lat=attacks_france[attacks_france.fatalities == 0]['latitude'], text=attacks_france[attacks_france.fatalities == 0]['text'], mode='markers', name='Injuries', hoverinfo='text+name', marker=dict(size=(attacks_france[attacks_france.fatalities == 0]['injuries'] + 1) ** 0.245 * 8, opacity=0.85, color='rgb(20, 150, 187)'))
layout = dict(title='Terrorist Attacks by Latitude/Longitude in France (1970-2015)', showlegend=True, legend=dict(x=0.85, y=0.4), geo=dict(scope='europe', projection=dict(type='albers usa'), showland=True, landcolor='rgb(250, 250, 250)', subunitwidth=1, subunitcolor='rgb(217, 217, 217)', countrywidth=1, countrycolor='rgb(217, 217, 217)', showlakes=True, lakecolor='rgb(255, 255, 255)'))
data = [fatality, injury]
figure = dict(data=data, layout=layout)
target_codes = []
for attack in attacks_france['target'].values:
if attack in ['Business', 'Journalists & Media', 'NGO']:
target_codes.append(1)
elif attack in ['Government (General)', 'Government (Diplomatic)']:
target_codes.append(2)
elif attack == 'Abortion Related':
target_codes.append(4)
elif attack == 'Educational Institution':
target_codes.append(5)
elif attack == 'Police':
target_codes.append(6)
elif attack == 'Military':
target_codes.append(7)
elif attack == 'Religious Figures/Institutions':
target_codes.append(8)
elif attack in ['Airports & Aircraft', 'Maritime', 'Transportation']:
target_codes.append(9)
elif attack in ['Food or Water Supply', 'Telecommunication', 'Utilities']:
target_codes.append(10)
else:
target_codes.append(3)
attacks_france['target'] = target_codes
target_categories = ['Business', 'Government', 'Individuals', 'Healthcare', 'Education', 'Police', 'Military', 'Religion', 'Transportation', 'Infrastructure']
target_count = np.asarray(attacks_france.groupby('target').target.count())
target_percent = np.round(target_count / sum(target_count) * 100, 2)
target_fatality = np.asarray(attacks_france.groupby('target')['fatalities'].sum())
target_yaxis = np.asarray([1.33, 2.36, 2.98, 0.81, 1.25, 1.71, 1.31, 1.53, 1.34, 0])
target_injury = np.asarray(attacks_france.groupby('target')['injuries'].sum())
target_xaxis = np.log10(target_injury)
target_text = []
for i in range(0, 10):
target_text.append(target_categories[i] + ' (' + target_percent[i].astype(str) + '%)<br>' + target_fatality[i].astype(str) + ' Killed, ' + target_injury[i].astype(str) + ' Injured')
data = [go.Scatter(x=target_injury, y=target_fatality, text=target_text, mode='markers', hoverinfo='text', marker=dict(size=target_count / 6.5, opacity=0.9, color='rgb(240, 140, 45)'))]
layout = go.Layout(title='Terrorist Attacks by Target in France (1970-2015)', xaxis=dict(title='Injuries', type='log', range=[1.36, 3.25], tickmode='auto', nticks=2, showline=True, showgrid=False), yaxis=dict(title='Fatalities', type='log', range=[0.59, 3.45], tickmode='auto', nticks=4, showline=True, showgrid=False))
annotations = []
for i in range(0, 10):
annotations.append(dict(x=target_xaxis[i], y=target_yaxis[i], xanchor='middle', yanchor='top', text=target_categories[i], showarrow=False))
layout['annotations'] = annotations
figure = dict(data=data, layout=layout)
iplot(figure) | code |
72065241/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sweetviz
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
report = sweetviz.analyze([train_data, 'train'], 'target') | code |
72065241/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 |
72065241/cell_3 | [
"text_html_output_1.png"
] | !pip install sweetviz | code |
72065241/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sweetviz
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
report = sweetviz.analyze([train_data, 'train'], 'target')
my_report = sweetviz.compare([train_data, 'train'], [test_data, 'test'], 'target') | code |
72065241/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sweetviz
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
report = sweetviz.analyze([train_data, 'train'], 'target')
report.show_notebook() | code |
130003745/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_27.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_24.png",
"text_plain_output_21.png",
"text_plain_output_25.png",
"text_plain_output_18.png",
"text_plain_output_3.png",
"text_plain_output_22.png",
"text_plain_output_7.png",
"text_plain_output_16.png",
"text_plain_output_8.png",
"text_plain_output_26.png",
"application_vnd.jupyter.stderr_output_20.png",
"text_plain_output_23.png",
"text_plain_output_28.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"text_plain_output_19.png",
"text_plain_output_17.png",
"text_plain_output_11.png",
"text_plain_output_12.png"
] | from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import fbeta_score,accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from time import time
from xgboost import XGBClassifier
import numpy as np # linear algebra
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
import numpy as np
import pandas as pd
import os
# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score
from sklearn.metrics import fbeta_score,accuracy_score
from time import time
def train_predict(learner, sample_size, X_train, y_train, X_test, y_test):
'''
inputs:
- learner: the learning algorithm to be trained and predicted on
- sample_size: the size of samples (number) to be drawn from training set
- X_train: features training set
- y_train: income training set
- X_test: features testing set
- y_test: income testing set
'''
results = {}
# TODO: Fit the learner to the training data using slicing with 'sample_size' using .fit(training_features[:], training_labels[:])
start = time() # Get start time
learner = learner.fit(X_train[:sample_size],y_train[:sample_size])
end = time() # Get end time
# TODO: Calculate the training time
results['train_time'] = end-start
# TODO: Get the predictions on the test set(X_test),
# then get predictions on the first 300 training samples(X_train) using .predict()
start = time() # Get start time
predictions_test = learner.predict(X_test)
predictions_train = learner.predict(X_train[:300])
end = time() # Get end time
# TODO: Calculate the total prediction time
results['pred_time'] = end-start
# TODO: Compute accuracy on the first 300 training samples which is y_train[:300]
results['acc_train'] = accuracy_score(y_train[:300],predictions_train)
# TODO: Compute accuracy on test set using accuracy_score()
results['acc_test'] = accuracy_score(y_test, predictions_test)
# TODO: Compute F-score on the the first 300 training samples using fbeta_score()
# results['f_train'] = fbeta_score(y_train[:300],predictions_train, beta=0.5)
# # TODO: Compute F-score on the test set which is y_test
# results['f_test'] = fbeta_score(y_test, predictions_test, beta=0.5)
# Success
print("{} trained on {} samples.".format(learner.__class__.__name__, sample_size))
# Return the results
return results
def evaluate(results):
"""
Visualization code to display results of various learners.
inputs:
- learners: a list of supervised learners
- stats: a list of dictionaries of the statistic results from 'train_predict()'
- accuracy: The score for the naive predictor
- f1: The score for the naive predictor
"""
# Create figure
fig, ax = pl.subplots(2, 3, figsize = (11,7))
# Constants
bar_width = 0.23
colors = ['#A00000','#00A0A0','#00A000', '#ffffff']
# Super loop to plot four panels of data
for k, learner in enumerate(results.keys()):
for j, metric in enumerate(['train_time', 'acc_train', 'f_train', 'pred_time', 'acc_test', 'f_test']):
for i in np.arange(3):
try:
# Creative plot code
ax[j//3, j%3].bar(i+k*bar_width, results[learner][i][metric], width = bar_width, color = colors[k])
ax[j//3, j%3].set_xticks([0.45, 1.45, 2.45])
ax[j//3, j%3].set_xticklabels(["1%", "10%", "100%"])
ax[j//3, j%3].set_xlabel("Training Set Size")
ax[j//3, j%3].set_xlim((-0.1, 3.0))
except:
print('+++',learner, i, metric,'+++')
# Add unique y-labels
ax[0, 0].set_ylabel("Time (in seconds)")
ax[0, 1].set_ylabel("Accuracy Score")
ax[0, 2].set_ylabel("F-score")
ax[1, 0].set_ylabel("Time (in seconds)")
ax[1, 1].set_ylabel("Accuracy Score")
ax[1, 2].set_ylabel("F-score")
# Add titles
ax[0, 0].set_title("Model Training")
ax[0, 1].set_title("Accuracy Score on Training Subset")
ax[0, 2].set_title("F-score on Training Subset")
ax[1, 0].set_title("Model Predicting")
ax[1, 1].set_title("Accuracy Score on Testing Set")
ax[1, 2].set_title("F-score on Testing Set")
# Add horizontal lines for naive predictors
ax[0, 1].axhline(y = accuracy, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
ax[1, 1].axhline(y = accuracy, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
ax[0, 2].axhline(y = f1, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
ax[1, 2].axhline(y = f1, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
# Set y-limits for score panels
ax[0, 1].set_ylim((0, 1))
ax[0, 2].set_ylim((0, 1))
ax[1, 1].set_ylim((0, 1))
ax[1, 2].set_ylim((0, 1))
# Create patches for the legend
patches = []
for i, learner in enumerate(results.keys()):
patches.append(mpatches.Patch(color = colors[i], label = learner))
pl.legend(handles = patches, bbox_to_anchor = (-.80, 2.53), \
loc = 'upper center', borderaxespad = 0., ncol = 3, fontsize = 'x-large')
# Aesthetics
pl.suptitle("Performance Metrics for Three Supervised Learning Models", fontsize = 16, y = 1.10)
pl.tight_layout()
pl.show()
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from lightgbm import LGBMClassifier
from catboost import CatBoostClassifier
from xgboost import XGBClassifier
import pandas as pd
from sklearn.model_selection import train_test_split
def train_df(filename):
df = pd.read_csv(filename, index_col=0)
m = list(df[' Label'].value_counts().index)
j = {}
h = 1
for i in m:
if i == 'BENIGN':
j[i] = 0
else:
j[i] = h
h += 1
df[' Label'] = [j[x] for x in df[' Label']]
df = df.dropna()
y = df[' Label']
X = df.drop(columns=['Flow ID', ' Label', ' Source IP', ' Destination IP', ' Timestamp', 'SimillarHTTP']).clip(-1000000.0, 1000000.0)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
clf_A = GradientBoostingClassifier(random_state=42)
clf_B = RandomForestClassifier(random_state=42)
clf_C = KNeighborsClassifier()
samples_100 = len(y_train)
samples_10 = int(samples_100 * 0.1)
samples_1 = int(samples_100 * 0.01)
results = {}
for clf in [clf_A, clf_B, clf_C]:
clf_name = clf.__class__.__name__
results[clf_name] = {}
for i, samples in enumerate([samples_1, samples_10, samples_100]):
results[clf_name][i] = train_predict(clf, samples, X_train, y_train, X_test, y_test)
clf_D = LGBMClassifier(random_state=42)
clf_E = CatBoostClassifier(random_state=42, verbose=False)
clf_F = XGBClassifier(random_state=42)
for clf in [clf_D, clf_E, clf_F]:
clf_name = clf.__class__.__name__
results[clf_name] = {}
for i, samples in enumerate([samples_1, samples_10, samples_100]):
results[clf_name][i] = train_predict(clf, samples, X_train, y_train, X_test, y_test)
return results
import pickle
import os, operator, sys
dirpath = '/kaggle/input/ddos-evaluation-dataset-cic-ddos2019/CSV-01-12/01-12'
all_files = (os.path.join(basedir, filename) for basedir, dirs, files in os.walk(dirpath) for filename in files)
files_and_sizes = ((path, os.path.getsize(path)) for path in all_files)
sorted_files_with_size = sorted(files_and_sizes, key=operator.itemgetter(1))
sorted_files_with_size
cont_flag = True
for filename, size in sorted_files_with_size:
if cont_flag:
if 'DrDoS_SSDP' in filename:
cont_flag = False
continue
res = train_df(filename)
pickle.dump(res, open('results_' + filename.split('/')[-1].replace('.csv', '') + '.p', 'wb'))
print(filename.split('/')[-1].replace('.csv', ''), ' Done\n') | code |
130003745/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import fbeta_score,accuracy_score
from time import time
import numpy as np # linear algebra
from sklearn.metrics import fbeta_score, accuracy_score
from time import time
def train_predict(learner, sample_size, X_train, y_train, X_test, y_test):
"""
inputs:
- learner: the learning algorithm to be trained and predicted on
- sample_size: the size of samples (number) to be drawn from training set
- X_train: features training set
- y_train: income training set
- X_test: features testing set
- y_test: income testing set
"""
results = {}
start = time()
learner = learner.fit(X_train[:sample_size], y_train[:sample_size])
end = time()
results['train_time'] = end - start
start = time()
predictions_test = learner.predict(X_test)
predictions_train = learner.predict(X_train[:300])
end = time()
results['pred_time'] = end - start
results['acc_train'] = accuracy_score(y_train[:300], predictions_train)
results['acc_test'] = accuracy_score(y_test, predictions_test)
print('{} trained on {} samples.'.format(learner.__class__.__name__, sample_size))
return results
def evaluate(results):
"""
Visualization code to display results of various learners.
inputs:
- learners: a list of supervised learners
- stats: a list of dictionaries of the statistic results from 'train_predict()'
- accuracy: The score for the naive predictor
- f1: The score for the naive predictor
"""
fig, ax = pl.subplots(2, 3, figsize=(11, 7))
bar_width = 0.23
colors = ['#A00000', '#00A0A0', '#00A000', '#ffffff']
for k, learner in enumerate(results.keys()):
for j, metric in enumerate(['train_time', 'acc_train', 'f_train', 'pred_time', 'acc_test', 'f_test']):
for i in np.arange(3):
try:
ax[j // 3, j % 3].bar(i + k * bar_width, results[learner][i][metric], width=bar_width, color=colors[k])
ax[j // 3, j % 3].set_xticks([0.45, 1.45, 2.45])
ax[j // 3, j % 3].set_xticklabels(['1%', '10%', '100%'])
ax[j // 3, j % 3].set_xlabel('Training Set Size')
ax[j // 3, j % 3].set_xlim((-0.1, 3.0))
except:
print('+++', learner, i, metric, '+++')
ax[0, 0].set_ylabel('Time (in seconds)')
ax[0, 1].set_ylabel('Accuracy Score')
ax[0, 2].set_ylabel('F-score')
ax[1, 0].set_ylabel('Time (in seconds)')
ax[1, 1].set_ylabel('Accuracy Score')
ax[1, 2].set_ylabel('F-score')
ax[0, 0].set_title('Model Training')
ax[0, 1].set_title('Accuracy Score on Training Subset')
ax[0, 2].set_title('F-score on Training Subset')
ax[1, 0].set_title('Model Predicting')
ax[1, 1].set_title('Accuracy Score on Testing Set')
ax[1, 2].set_title('F-score on Testing Set')
ax[0, 1].axhline(y=accuracy, xmin=-0.1, xmax=3.0, linewidth=1, color='k', linestyle='dashed')
ax[1, 1].axhline(y=accuracy, xmin=-0.1, xmax=3.0, linewidth=1, color='k', linestyle='dashed')
ax[0, 2].axhline(y=f1, xmin=-0.1, xmax=3.0, linewidth=1, color='k', linestyle='dashed')
ax[1, 2].axhline(y=f1, xmin=-0.1, xmax=3.0, linewidth=1, color='k', linestyle='dashed')
ax[0, 1].set_ylim((0, 1))
ax[0, 2].set_ylim((0, 1))
ax[1, 1].set_ylim((0, 1))
ax[1, 2].set_ylim((0, 1))
patches = []
for i, learner in enumerate(results.keys()):
patches.append(mpatches.Patch(color=colors[i], label=learner))
pl.legend(handles=patches, bbox_to_anchor=(-0.8, 2.53), loc='upper center', borderaxespad=0.0, ncol=3, fontsize='x-large')
pl.suptitle('Performance Metrics for Three Supervised Learning Models', fontsize=16, y=1.1)
pl.tight_layout()
pl.show() | code |
130003745/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 |
327044/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csva') | code |
327044/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
34120494/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
diabetes_data = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
diabetes_data[:60]
diabetes_data.shape
diabetes_data.isna().sum()
diabetes_data.dtypes | code |
34120494/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.svm import LinearSVC
svc_model = LinearSVC(max_iter=10000)
svc_model.fit(X_train, y_train) | code |
34120494/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
diabetes_data = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
diabetes_data.head() | code |
34120494/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
import numpy as np
svc_model = LinearSVC(max_iter=10000)
svc_model.fit(X_train, y_train)
svc_score = svc_model.score(X_test, y_test)
if svc_score < 0.6:
print(f'SVC Model Score is Less : {svc_score}'.format())
else:
random_clf = RandomForestClassifier(n_estimators=100)
random_clf.fit(X_train, y_train)
patient_sample = np.array([[0, 137, 40, 35, 168, 43.1, 2.244, 30]])
prediction = random_clf.predict(patient_sample)
if prediction == 0:
print('You are not expected to be diabetic')
elif prediction == 1:
print('You are expected to be diabetic') | code |
34120494/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
diabetes_data = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
diabetes_data[:60]
diabetes_data.shape | code |
104119796/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/destiny-2-guns/guns.csv')
features = ['Element ', 'Rarity']
fig, ax = plt.subplots(1, len(features), figsize=(16, 5), sharex=False)
for cnt, feature in enumerate(['Element ', 'Rarity']):
df.groupby(['weapon_type', feature]).size().unstack().plot(kind='barh', stacked=True, ax=ax[cnt])
plt.tight_layout()
plt.show() | code |
104119796/cell_1 | [
"text_plain_output_1.png"
] | import os
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import warnings
sns.set_style('darkgrid')
warnings.filterwarnings('ignore')
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
104119796/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import warnings
sns.set_style('darkgrid')
warnings.filterwarnings('ignore')
df = pd.read_csv('/kaggle/input/destiny-2-guns/guns.csv')
# count of weapons, breakdown by Element and Rarity
features = ['Element ','Rarity']
fig, ax = plt.subplots(1,len(features),figsize=(16,5), sharex=False)
for cnt, feature in enumerate(['Element ','Rarity']):
df.groupby(['weapon_type',feature]).size().unstack().plot(kind='barh', stacked=True, ax=ax[cnt])
plt.tight_layout()
plt.show()
features = ['Element ', 'Rarity', 'weapon_type']
fig, ax = plt.subplots(2, 3, figsize=(18, 8))
for cnt, f in enumerate(features):
current_col = int(cnt % 3)
tmp = df[[f, 'gun_RoF']].sort_values(by=['gun_RoF'], ascending=True)
sns.boxplot(ax=ax[0, current_col], data=tmp, x='gun_RoF', y=f, linewidth=2, showfliers=False)
sns.histplot(ax=ax[1, current_col], data=tmp, x='gun_RoF', hue=f, kde=True, bins=20, legend=False, line_kws={'lw': 2}, alpha=0.5)
plt.tight_layout()
plt.show() | code |
88103172/cell_4 | [
"text_plain_output_1.png"
] | from aitextgen import aitextgen
from aitextgen.TokenDataset import TokenDataset
from aitextgen.tokenizers import train_tokenizer
from aitextgen.utils import GPTNeoConfigCPU
file_name = '/kaggle/input/annomi/dataset.txt'
train_tokenizer(file_name)
tokenizer_file = 'aitextgen.tokenizer.json'
config = GPTNeoConfigCPU()
ai = aitextgen(tokenizer_file=tokenizer_file, config=config)
data = TokenDataset(file_name, tokenizer_file=tokenizer_file, block_size=64) | code |
88103172/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from aitextgen import aitextgen
from aitextgen.TokenDataset import TokenDataset
from aitextgen.tokenizers import train_tokenizer
from aitextgen.utils import GPTNeoConfigCPU
file_name = '/kaggle/input/annomi/dataset.txt'
train_tokenizer(file_name)
tokenizer_file = 'aitextgen.tokenizer.json'
config = GPTNeoConfigCPU()
ai = aitextgen(tokenizer_file=tokenizer_file, config=config)
data = TokenDataset(file_name, tokenizer_file=tokenizer_file, block_size=64)
ai.train(data, batch_size=16, num_steps=50000, generate_every=1000, save_every=1000) | code |
88103172/cell_1 | [
"text_plain_output_1.png"
] | ! pip install aitextgen | code |
88103172/cell_7 | [
"text_plain_output_1.png"
] | from aitextgen import aitextgen
ai2 = aitextgen(model_folder='./trained_model', tokenizer_file='aitextgen.tokenizer.json')
ai2.generate(10, prompt='<START>\nTHERAPIST:\nHi, Emily.\nCLIENT:\nHi. I am feeling low and have been drinking alcohol every day. What do you think that is happening\n') | code |
50240545/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/catch-me-if-you-can/train_sessions.csv', index_col='session_id')
test_df = pd.read_csv('../input/catch-me-if-you-can/test_sessions.csv', index_col='session_id')
features = pd.DataFrame()
timepoints = train_df[['time%s' % i for i in range(1, 11)]]
sites = train_df[['site%s' % i for i in range(1, 11)]].fillna(0).astype(int).values
for td_index in range(1, 10):
features['time_diff{}'.format(td_index)] = (pd.to_datetime(timepoints['time{}'.format(td_index + 1)]) - pd.to_datetime(timepoints['time{}'.format(td_index)])).dt.total_seconds().fillna(0)
features['time_of_session'] = np.sum(features, axis=1)
features['hour'] = pd.to_datetime(timepoints['time1']).dt.hour
features['day_of_week'] = pd.to_datetime(timepoints['time1']).dt.weekday
features['unique_sites'] = [len(np.unique(session[session != 0])) for session in sites]
plt.figure(figsize=(15, 10))
sns.distplot(features['time_of_session'], label='time')
plt.ylabel('Кол-во')
plt.xlabel('Продолжительность сессии')
plt.grid() | code |
50240545/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/catch-me-if-you-can/train_sessions.csv', index_col='session_id')
test_df = pd.read_csv('../input/catch-me-if-you-can/test_sessions.csv', index_col='session_id')
train_df.head() | code |
50240545/cell_5 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/catch-me-if-you-can/train_sessions.csv', index_col='session_id')
test_df = pd.read_csv('../input/catch-me-if-you-can/test_sessions.csv', index_col='session_id')
features = pd.DataFrame()
timepoints = train_df[['time%s' % i for i in range(1, 11)]]
sites = train_df[['site%s' % i for i in range(1, 11)]].fillna(0).astype(int).values
for td_index in range(1, 10):
features['time_diff{}'.format(td_index)] = (pd.to_datetime(timepoints['time{}'.format(td_index + 1)]) - pd.to_datetime(timepoints['time{}'.format(td_index)])).dt.total_seconds().fillna(0)
features['time_of_session'] = np.sum(features, axis=1)
features['hour'] = pd.to_datetime(timepoints['time1']).dt.hour
features['day_of_week'] = pd.to_datetime(timepoints['time1']).dt.weekday
features['unique_sites'] = [len(np.unique(session[session != 0])) for session in sites]
features.head() | code |
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