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18159957/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
labels_house = ['yes', 'no', 'unknown']
sizes_house = [2175, 1839, 105]
colors_house = ['#ff6666', '#ffcc99', '#ffb3e6']
labels_loan = ['yes', 'no', 'unknown']
sizes_loan = [665, 3349, 105]
colors_loan = ['#c2c2f0', '#ffb3e6', '#66b3ff']
labels_contact = ['cellular', 'telephone']
sizes_contact = [2652, 1467]
colors_contact = ['#ff9999', '#ffcc99']
labels_default = ['no', 'unknown', 'yes']
sizes_default = [3523, 454, 142]
colors_default = ['#99ff99', '#66b3ff', '#ff6666']
plt.rcParams.update({'font.size': 15})
plt.figure(0)
plt.pie(sizes_house, labels=labels_house, colors=colors_house, autopct='%1.1f%%', startangle=90, pctdistance=0.8)
plt.title('Housing Loan')
centre_circle = plt.Circle((0, 0), 0.5, color='black', fc='white', linewidth=0)
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.show()
plt.figure(1)
plt.pie(sizes_loan, labels=labels_loan, colors=colors_loan, autopct='%1.1f%%', startangle=90, pctdistance=0.8)
plt.title('Personal Loan')
centre_circle = plt.Circle((0, 0), 0.5, color='black', fc='white', linewidth=0)
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.show()
plt.figure(2)
plt.pie(sizes_contact, labels=labels_contact, colors=colors_contact, autopct='%1.1f%%', startangle=90, pctdistance=0.8)
plt.title('Contact Method')
centre_circle = plt.Circle((0, 0), 0.5, color='black', fc='white', linewidth=0)
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.show()
plt.figure(3)
plt.pie(sizes_default, labels=labels_default, colors=colors_default, autopct='%1.1f%%', startangle=90, pctdistance=0.8)
plt.title('default')
centre_circle = plt.Circle((0, 0), 0.5, color='black', fc='white', linewidth=0)
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.show() | code |
16122877/cell_21 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import TfidfVectorizer
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/taylor_swift_lyrics.csv', encoding='latin-1')
pd.options.display.max_colwidth = 5000
songs = df.groupby('track_title').agg({'lyric': lambda x: ' '.join(x), 'year': 'mean'}).reset_index()
stop_words = stopwords.words('english')
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words=stop_words, min_df=0.1)
stop_words.extend(['back', 'said', 'come', 'things', 'get', 'oh', 'one', 'yeah', 'place', 'would', 'like', 'know', 'stay', 'go', 'let', 'cause', 'could', 'wanna', 'would', 'gonna'])
tfidf = vectorizer.fit_transform(songs['lyric'])
nmf = NMF(n_components=6)
topic_values = nmf.fit_transform(tfidf)
for topic_num, topic in enumerate(nmf.components_):
message = 'Topc #{}:'.format(topic_num + 1)
message += ' '.join([vectorizer.get_feature_names()[i] for i in topic.argsort()[:-11:-1]])
topic_labels = ['love/beauty', 'growing up', 'home', 'bad/remorse', 'hope/better', 'party/dance']
df_topics = pd.DataFrame(topic_values, columns=topic_labels)
songs = songs.join(df_topics)
for topic in topic_labels:
songs.loc[songs[topic] >= 0.1, topic] = 1
songs.loc[songs[topic] < 0.1, topic] = 0
year_topics = songs.groupby('year').sum().reset_index()
year_topics
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams.update({'font.size': 30, 'lines.linewidth': 8})
plt.figure(figsize=(30, 15))
plt.grid(True)
for topic in topic_labels:
plt.plot(year_topics['year'], year_topics[topic], label=topic, linewidth=7.0)
plt.legend()
plt.xlabel('year')
plt.ylabel('# of songs per topic')
plt.title("Topic modeling of Taylor Swift's lyrics")
plt.show() | code |
16122877/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/taylor_swift_lyrics.csv', encoding='latin-1')
songs = df.groupby('track_title').agg({'lyric': lambda x: ' '.join(x), 'year': 'mean'}).reset_index()
len(songs) | code |
16122877/cell_20 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/taylor_swift_lyrics.csv', encoding='latin-1')
pd.options.display.max_colwidth = 5000
songs = df.groupby('track_title').agg({'lyric': lambda x: ' '.join(x), 'year': 'mean'}).reset_index()
stop_words = stopwords.words('english')
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words=stop_words, min_df=0.1)
stop_words.extend(['back', 'said', 'come', 'things', 'get', 'oh', 'one', 'yeah', 'place', 'would', 'like', 'know', 'stay', 'go', 'let', 'cause', 'could', 'wanna', 'would', 'gonna'])
tfidf = vectorizer.fit_transform(songs['lyric'])
nmf = NMF(n_components=6)
topic_values = nmf.fit_transform(tfidf)
for topic_num, topic in enumerate(nmf.components_):
message = 'Topc #{}:'.format(topic_num + 1)
message += ' '.join([vectorizer.get_feature_names()[i] for i in topic.argsort()[:-11:-1]])
topic_labels = ['love/beauty', 'growing up', 'home', 'bad/remorse', 'hope/better', 'party/dance']
df_topics = pd.DataFrame(topic_values, columns=topic_labels)
songs = songs.join(df_topics)
for topic in topic_labels:
songs.loc[songs[topic] >= 0.1, topic] = 1
songs.loc[songs[topic] < 0.1, topic] = 0
year_topics = songs.groupby('year').sum().reset_index()
year_topics | code |
16122877/cell_19 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/taylor_swift_lyrics.csv', encoding='latin-1')
pd.options.display.max_colwidth = 5000
songs = df.groupby('track_title').agg({'lyric': lambda x: ' '.join(x), 'year': 'mean'}).reset_index()
stop_words = stopwords.words('english')
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words=stop_words, min_df=0.1)
stop_words.extend(['back', 'said', 'come', 'things', 'get', 'oh', 'one', 'yeah', 'place', 'would', 'like', 'know', 'stay', 'go', 'let', 'cause', 'could', 'wanna', 'would', 'gonna'])
tfidf = vectorizer.fit_transform(songs['lyric'])
nmf = NMF(n_components=6)
topic_values = nmf.fit_transform(tfidf)
for topic_num, topic in enumerate(nmf.components_):
message = 'Topc #{}:'.format(topic_num + 1)
message += ' '.join([vectorizer.get_feature_names()[i] for i in topic.argsort()[:-11:-1]])
topic_labels = ['love/beauty', 'growing up', 'home', 'bad/remorse', 'hope/better', 'party/dance']
df_topics = pd.DataFrame(topic_values, columns=topic_labels)
songs = songs.join(df_topics)
for topic in topic_labels:
songs.loc[songs[topic] >= 0.1, topic] = 1
songs.loc[songs[topic] < 0.1, topic] = 0
songs.head() | code |
16122877/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/taylor_swift_lyrics.csv', encoding='latin-1')
df.head() | code |
16122877/cell_16 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/taylor_swift_lyrics.csv', encoding='latin-1')
pd.options.display.max_colwidth = 5000
songs = df.groupby('track_title').agg({'lyric': lambda x: ' '.join(x), 'year': 'mean'}).reset_index()
stop_words = stopwords.words('english')
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words=stop_words, min_df=0.1)
stop_words.extend(['back', 'said', 'come', 'things', 'get', 'oh', 'one', 'yeah', 'place', 'would', 'like', 'know', 'stay', 'go', 'let', 'cause', 'could', 'wanna', 'would', 'gonna'])
tfidf = vectorizer.fit_transform(songs['lyric'])
nmf = NMF(n_components=6)
topic_values = nmf.fit_transform(tfidf)
topic_labels = ['love/beauty', 'growing up', 'home', 'bad/remorse', 'hope/better', 'party/dance']
df_topics = pd.DataFrame(topic_values, columns=topic_labels)
df_topics.head() | code |
16122877/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/taylor_swift_lyrics.csv', encoding='latin-1')
songs = df.groupby('track_title').agg({'lyric': lambda x: ' '.join(x), 'year': 'mean'}).reset_index()
songs.head() | code |
16122877/cell_17 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/taylor_swift_lyrics.csv', encoding='latin-1')
pd.options.display.max_colwidth = 5000
songs = df.groupby('track_title').agg({'lyric': lambda x: ' '.join(x), 'year': 'mean'}).reset_index()
stop_words = stopwords.words('english')
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words=stop_words, min_df=0.1)
stop_words.extend(['back', 'said', 'come', 'things', 'get', 'oh', 'one', 'yeah', 'place', 'would', 'like', 'know', 'stay', 'go', 'let', 'cause', 'could', 'wanna', 'would', 'gonna'])
tfidf = vectorizer.fit_transform(songs['lyric'])
nmf = NMF(n_components=6)
topic_values = nmf.fit_transform(tfidf)
topic_labels = ['love/beauty', 'growing up', 'home', 'bad/remorse', 'hope/better', 'party/dance']
df_topics = pd.DataFrame(topic_values, columns=topic_labels)
songs = songs.join(df_topics)
songs.head() | code |
16122877/cell_14 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/taylor_swift_lyrics.csv', encoding='latin-1')
songs = df.groupby('track_title').agg({'lyric': lambda x: ' '.join(x), 'year': 'mean'}).reset_index()
stop_words = stopwords.words('english')
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words=stop_words, min_df=0.1)
stop_words.extend(['back', 'said', 'come', 'things', 'get', 'oh', 'one', 'yeah', 'place', 'would', 'like', 'know', 'stay', 'go', 'let', 'cause', 'could', 'wanna', 'would', 'gonna'])
tfidf = vectorizer.fit_transform(songs['lyric'])
nmf = NMF(n_components=6)
topic_values = nmf.fit_transform(tfidf)
for topic_num, topic in enumerate(nmf.components_):
message = 'Topc #{}:'.format(topic_num + 1)
message += ' '.join([vectorizer.get_feature_names()[i] for i in topic.argsort()[:-11:-1]])
print(message) | code |
327861/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
f, axarr = plt.subplots(10, 10)
for row in range(10):
for column in range(10):
entry = train_data[train_data['label']==column].iloc[row].drop('label').as_matrix()
axarr[row, column].imshow(entry.reshape([28, 28]))
axarr[row, column].get_xaxis().set_visible(False)
axarr[row, column].get_yaxis().set_visible(False)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
train_val_ratio = 0.7
train_data_size = len(train_data)
train_set = train_data[:int(train_data_size * train_val_ratio)]
val_set = train_data[int(train_data_size * train_val_ratio) + 1:]
init = tf.initialize_all_variables()
saver = tf.train.Saver()
sess = tf.Session()
sess.run(init)
train_eval_list = []
val_eval_list = []
for i in range(1000):
batch = train_set.sample(frac=0.1)
batch_xs = batch.drop('label', axis=1).as_matrix() / 255.0
batch_ys = pd.get_dummies(batch['label']).as_matrix()
val_xs = val_set.drop('label', axis=1).as_matrix() / 255.0
val_ys = pd.get_dummies(val_set['label']).as_matrix()
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
train_eval = sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys})
val_eval = sess.run(accuracy, feed_dict={x: val_xs, y_: val_ys})
train_eval_list.append(train_eval)
val_eval_list.append(val_eval)
saver.save(sess, 'logistic_regression.ckpt')
sess.close()
plt.plot(train_eval_list, label='Train set')
plt.plot(val_eval_list, label='Validation set')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(loc=4) | code |
327861/cell_3 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
print(train_data.shape)
print(test_data.shape) | code |
327861/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
f, axarr = plt.subplots(10, 10)
for row in range(10):
for column in range(10):
entry = train_data[train_data['label'] == column].iloc[row].drop('label').as_matrix()
axarr[row, column].imshow(entry.reshape([28, 28]))
axarr[row, column].get_xaxis().set_visible(False)
axarr[row, column].get_yaxis().set_visible(False) | code |
33095970/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
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 re
import warnings
papers = pd.read_csv('/kaggle/input/nips-papers/papers.csv')
papers_2010 = papers.loc[papers['year'] == 2010].copy(deep=False)
papers_2017 = papers.loc[papers['year'] == 2017].copy(deep=False)
import re
papers_2010['title_processed'] = papers_2010['title'].map(lambda x: re.sub('[,\\.!?]', '', x))
papers_2017['title_processed'] = papers_2017['title'].map(lambda x: re.sub('[,\\.!?]', '', x))
papers_2010['title_processed'] = papers_2010['title_processed'].map(lambda x: x.lower())
papers_2017['title_processed'] = papers_2017['title_processed'].map(lambda x: x.lower())
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
def plot_10_most_common_words(count_data, count_vectorizer):
import matplotlib.pyplot as plt
words = count_vectorizer.get_feature_names()
total_counts = np.zeros(len(words))
for t in count_data:
total_counts += t.toarray()[0]
count_dict = zip(words, total_counts)
count_dict = sorted(count_dict, key=lambda x: x[1], reverse=True)[1:51]
words = [w[0] for w in count_dict]
counts = [w[1] for w in count_dict]
x_pos = np.arange(len(words))
plt.xticks(x_pos, words, rotation=90)
count_vectorizer = CountVectorizer(stop_words='english')
count_data = count_vectorizer.fit_transform(papers_2010['title_processed'])
dict = [count_data, count_vectorizer]
import warnings
warnings.simplefilter('ignore', DeprecationWarning)
from sklearn.decomposition import LatentDirichletAllocation as LDA
def print_topics(model, count_vectorizer, n_top_words):
words = count_vectorizer.get_feature_names()
for topic_idx, topic in enumerate(model.components_):
print('\nTopic #%d:' % topic_idx)
print(' '.join([words[i] for i in topic.argsort()[:-n_top_words - 1:-1]]))
number_topics = 5
number_words = 6
lda = LDA(n_components=number_topics)
lda.fit(count_data)
print('Topics found via LDA:')
print_topics(lda, count_vectorizer, number_words) | code |
33095970/cell_2 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
papers = pd.read_csv('/kaggle/input/nips-papers/papers.csv')
print(type(papers)) | code |
33095970/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
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 re
import warnings
import warnings
papers = pd.read_csv('/kaggle/input/nips-papers/papers.csv')
papers_2010 = papers.loc[papers['year'] == 2010].copy(deep=False)
papers_2017 = papers.loc[papers['year'] == 2017].copy(deep=False)
import re
papers_2010['title_processed'] = papers_2010['title'].map(lambda x: re.sub('[,\\.!?]', '', x))
papers_2017['title_processed'] = papers_2017['title'].map(lambda x: re.sub('[,\\.!?]', '', x))
papers_2010['title_processed'] = papers_2010['title_processed'].map(lambda x: x.lower())
papers_2017['title_processed'] = papers_2017['title_processed'].map(lambda x: x.lower())
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
def plot_10_most_common_words(count_data, count_vectorizer):
import matplotlib.pyplot as plt
words = count_vectorizer.get_feature_names()
total_counts = np.zeros(len(words))
for t in count_data:
total_counts += t.toarray()[0]
count_dict = zip(words, total_counts)
count_dict = sorted(count_dict, key=lambda x: x[1], reverse=True)[1:51]
words = [w[0] for w in count_dict]
counts = [w[1] for w in count_dict]
x_pos = np.arange(len(words))
plt.xticks(x_pos, words, rotation=90)
count_vectorizer = CountVectorizer(stop_words='english')
count_data = count_vectorizer.fit_transform(papers_2010['title_processed'])
dict = [count_data, count_vectorizer]
import warnings
warnings.simplefilter('ignore', DeprecationWarning)
from sklearn.decomposition import LatentDirichletAllocation as LDA
def print_topics(model, count_vectorizer, n_top_words):
words = count_vectorizer.get_feature_names()
number_topics = 5
number_words = 6
lda = LDA(n_components=number_topics)
lda.fit(count_data)
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
def plot_50_most_common_words(count_data, count_vectorizer):
import matplotlib.pyplot as plt
words = count_vectorizer.get_feature_names()
total_counts = np.zeros(len(words))
for t in count_data:
total_counts += t.toarray()[0]
count_dict = zip(words, total_counts)
count_dict = sorted(count_dict, key=lambda x: x[1], reverse=True)[1:21]
words = [w[0] for w in count_dict]
counts = [w[1] for w in count_dict]
x_pos = np.arange(len(words))
plt.xticks(x_pos, words, rotation=90)
count_vectorizer = CountVectorizer(stop_words='english')
count_data = count_vectorizer.fit_transform(papers_2017['title_processed'])
count_data.shape
import warnings
warnings.simplefilter('ignore', DeprecationWarning)
from sklearn.decomposition import LatentDirichletAllocation as LDA
def print_topics(model, count_vectorizer, n_top_words):
words = count_vectorizer.get_feature_names()
for topic_idx, topic in enumerate(model.components_):
print('\nTopic #%d:' % topic_idx)
print(' '.join([words[i] for i in topic.argsort()[:-n_top_words - 1:-1]]))
number_topics = 5
number_words = 6
lda = LDA(n_components=number_topics)
lda.fit(count_data)
print('Topics found via LDA:')
print_topics(lda, count_vectorizer, number_words) | code |
33095970/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import nltk
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33095970/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
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 re
papers = pd.read_csv('/kaggle/input/nips-papers/papers.csv')
papers_2010 = papers.loc[papers['year'] == 2010].copy(deep=False)
papers_2017 = papers.loc[papers['year'] == 2017].copy(deep=False)
import re
papers_2010['title_processed'] = papers_2010['title'].map(lambda x: re.sub('[,\\.!?]', '', x))
papers_2017['title_processed'] = papers_2017['title'].map(lambda x: re.sub('[,\\.!?]', '', x))
papers_2010['title_processed'] = papers_2010['title_processed'].map(lambda x: x.lower())
papers_2017['title_processed'] = papers_2017['title_processed'].map(lambda x: x.lower())
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
def plot_10_most_common_words(count_data, count_vectorizer):
import matplotlib.pyplot as plt
words = count_vectorizer.get_feature_names()
total_counts = np.zeros(len(words))
for t in count_data:
total_counts += t.toarray()[0]
count_dict = zip(words, total_counts)
count_dict = sorted(count_dict, key=lambda x: x[1], reverse=True)[1:51]
words = [w[0] for w in count_dict]
counts = [w[1] for w in count_dict]
x_pos = np.arange(len(words))
plt.bar(x_pos, counts, align='center')
plt.xticks(x_pos, words, rotation=90)
plt.xlabel('words')
plt.ylabel('counts')
plt.title('20 most common words')
plt.show()
count_vectorizer = CountVectorizer(stop_words='english')
count_data = count_vectorizer.fit_transform(papers_2010['title_processed'])
plot_10_most_common_words(count_data, count_vectorizer)
dict = [count_data, count_vectorizer] | code |
33095970/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
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 re
papers = pd.read_csv('/kaggle/input/nips-papers/papers.csv')
papers_2010 = papers.loc[papers['year'] == 2010].copy(deep=False)
papers_2017 = papers.loc[papers['year'] == 2017].copy(deep=False)
import re
papers_2010['title_processed'] = papers_2010['title'].map(lambda x: re.sub('[,\\.!?]', '', x))
papers_2017['title_processed'] = papers_2017['title'].map(lambda x: re.sub('[,\\.!?]', '', x))
papers_2010['title_processed'] = papers_2010['title_processed'].map(lambda x: x.lower())
papers_2017['title_processed'] = papers_2017['title_processed'].map(lambda x: x.lower())
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
def plot_10_most_common_words(count_data, count_vectorizer):
import matplotlib.pyplot as plt
words = count_vectorizer.get_feature_names()
total_counts = np.zeros(len(words))
for t in count_data:
total_counts += t.toarray()[0]
count_dict = zip(words, total_counts)
count_dict = sorted(count_dict, key=lambda x: x[1], reverse=True)[1:51]
words = [w[0] for w in count_dict]
counts = [w[1] for w in count_dict]
x_pos = np.arange(len(words))
plt.xticks(x_pos, words, rotation=90)
count_vectorizer = CountVectorizer(stop_words='english')
count_data = count_vectorizer.fit_transform(papers_2010['title_processed'])
dict = [count_data, count_vectorizer]
dict | code |
33095970/cell_3 | [
"text_plain_output_1.png"
] | groups = papers.groupby('year')
counts = groups.size()
import matplotlib.pyplot
counts.plot() | code |
33095970/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
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 re
import warnings
papers = pd.read_csv('/kaggle/input/nips-papers/papers.csv')
papers_2010 = papers.loc[papers['year'] == 2010].copy(deep=False)
papers_2017 = papers.loc[papers['year'] == 2017].copy(deep=False)
import re
papers_2010['title_processed'] = papers_2010['title'].map(lambda x: re.sub('[,\\.!?]', '', x))
papers_2017['title_processed'] = papers_2017['title'].map(lambda x: re.sub('[,\\.!?]', '', x))
papers_2010['title_processed'] = papers_2010['title_processed'].map(lambda x: x.lower())
papers_2017['title_processed'] = papers_2017['title_processed'].map(lambda x: x.lower())
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
def plot_10_most_common_words(count_data, count_vectorizer):
import matplotlib.pyplot as plt
words = count_vectorizer.get_feature_names()
total_counts = np.zeros(len(words))
for t in count_data:
total_counts += t.toarray()[0]
count_dict = zip(words, total_counts)
count_dict = sorted(count_dict, key=lambda x: x[1], reverse=True)[1:51]
words = [w[0] for w in count_dict]
counts = [w[1] for w in count_dict]
x_pos = np.arange(len(words))
plt.xticks(x_pos, words, rotation=90)
count_vectorizer = CountVectorizer(stop_words='english')
count_data = count_vectorizer.fit_transform(papers_2010['title_processed'])
dict = [count_data, count_vectorizer]
import warnings
warnings.simplefilter('ignore', DeprecationWarning)
from sklearn.decomposition import LatentDirichletAllocation as LDA
def print_topics(model, count_vectorizer, n_top_words):
words = count_vectorizer.get_feature_names()
number_topics = 5
number_words = 6
lda = LDA(n_components=number_topics)
lda.fit(count_data)
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
def plot_50_most_common_words(count_data, count_vectorizer):
import matplotlib.pyplot as plt
words = count_vectorizer.get_feature_names()
total_counts = np.zeros(len(words))
for t in count_data:
total_counts += t.toarray()[0]
count_dict = zip(words, total_counts)
count_dict = sorted(count_dict, key=lambda x: x[1], reverse=True)[1:21]
words = [w[0] for w in count_dict]
counts = [w[1] for w in count_dict]
x_pos = np.arange(len(words))
plt.bar(x_pos, counts, align='center')
plt.xticks(x_pos, words, rotation=90)
plt.xlabel('words')
plt.ylabel('counts')
plt.title('50 most common words')
plt.show()
count_vectorizer = CountVectorizer(stop_words='english')
count_data = count_vectorizer.fit_transform(papers_2017['title_processed'])
plot_50_most_common_words(count_data, count_vectorizer)
count_data.shape | code |
122261632/cell_63 | [
"text_plain_output_1.png"
] | print('Shape of X_test', X_test.shape) | code |
122261632/cell_57 | [
"text_plain_output_1.png"
] | from imblearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np
cat_pipe = Pipeline([('imputer', SimpleImputer(strategy='most_frequent', missing_values=np.nan)), ('encoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('scaler', StandardScaler())])
num_pipe = Pipeline([('imputer', SimpleImputer(strategy='most_frequent', missing_values=np.nan)), ('scaler', StandardScaler())])
print(num_pipe) | code |
122261632/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
train_df = pd.merge(train_df_sample, train_label_df, how='inner', on=['customer_ID'])
train_df.drop(axis=1, columns=['customer_ID', 'S_2'], inplace=True)
if any(train_df.duplicated()):
print('Yes')
else:
print('No') | code |
122261632/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
print('Shape of dataset is:', train_label_df.shape)
train_label_df.info() | code |
122261632/cell_55 | [
"text_plain_output_1.png"
] | from imblearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np
cat_pipe = Pipeline([('imputer', SimpleImputer(strategy='most_frequent', missing_values=np.nan)), ('encoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('scaler', StandardScaler())])
print(cat_pipe) | code |
122261632/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from catboost import CatBoostClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import classification_report
from sklearn.metrics import make_scorer
from sklearn.metrics import confusion_matrix
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import RepeatedKFold
from sklearn.model_selection import cross_validate
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import validation_curve
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from yellowbrick.model_selection import ValidationCurve | code |
122261632/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
test_df.drop(axis=1, columns=['S_2'], inplace=True)
if any(test_df.isna().sum()):
print('Yes')
else:
print('No') | code |
122261632/cell_65 | [
"text_plain_output_1.png"
] | print('Shape of y_test', y_test.shape) | code |
122261632/cell_50 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
train_df = pd.merge(train_df_sample, train_label_df, how='inner', on=['customer_ID'])
train_df.drop(axis=1, columns=['customer_ID', 'S_2'], inplace=True)
test_df.drop(axis=1, columns=['S_2'], inplace=True)
i = 0
for col in train_df.columns:
if train_df[col].isnull().sum() / len(train_df[col]) * 100 >= 75:
train_df.drop(labels=col, axis=1, inplace=True)
i = i + 1
i = 0
for col in test_df.columns:
if test_df[col].isnull().sum() / len(test_df[col]) * 100 >= 75:
test_df.drop(labels=col, axis=1, inplace=True)
i = i + 1
train_df = train_df.astype({'B_30': 'str', 'B_38': 'str'})
test_df = test_df.astype({'B_30': 'str', 'B_38': 'str'})
train_df = train_df.astype({'D_114': 'str', 'D_116': 'str', 'D_117': 'str', 'D_120': 'str', 'D_126': 'str', 'D_68': 'str'})
test_df = test_df.astype({'D_114': 'str', 'D_116': 'str', 'D_117': 'str', 'D_120': 'str', 'D_126': 'str', 'D_68': 'str'})
X = train_df.drop(columns='target')
y = train_df['target']
print('Shape of X', X.shape) | code |
122261632/cell_64 | [
"text_plain_output_1.png"
] | print('Shape of y_train', y_train.shape) | code |
122261632/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
train_df = pd.merge(train_df_sample, train_label_df, how='inner', on=['customer_ID'])
train_df.drop(axis=1, columns=['customer_ID', 'S_2'], inplace=True)
test_df.drop(axis=1, columns=['S_2'], inplace=True)
i = 0
for col in train_df.columns:
if train_df[col].isnull().sum() / len(train_df[col]) * 100 >= 75:
train_df.drop(labels=col, axis=1, inplace=True)
i = i + 1
i = 0
for col in test_df.columns:
if test_df[col].isnull().sum() / len(test_df[col]) * 100 >= 75:
print('Dropping column', col)
test_df.drop(labels=col, axis=1, inplace=True)
i = i + 1
print('Total number of columns dropped in test dataframe', i) | code |
122261632/cell_51 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
train_df = pd.merge(train_df_sample, train_label_df, how='inner', on=['customer_ID'])
train_df.drop(axis=1, columns=['customer_ID', 'S_2'], inplace=True)
test_df.drop(axis=1, columns=['S_2'], inplace=True)
i = 0
for col in train_df.columns:
if train_df[col].isnull().sum() / len(train_df[col]) * 100 >= 75:
train_df.drop(labels=col, axis=1, inplace=True)
i = i + 1
i = 0
for col in test_df.columns:
if test_df[col].isnull().sum() / len(test_df[col]) * 100 >= 75:
test_df.drop(labels=col, axis=1, inplace=True)
i = i + 1
train_df = train_df.astype({'B_30': 'str', 'B_38': 'str'})
test_df = test_df.astype({'B_30': 'str', 'B_38': 'str'})
train_df = train_df.astype({'D_114': 'str', 'D_116': 'str', 'D_117': 'str', 'D_120': 'str', 'D_126': 'str', 'D_68': 'str'})
test_df = test_df.astype({'D_114': 'str', 'D_116': 'str', 'D_117': 'str', 'D_120': 'str', 'D_126': 'str', 'D_68': 'str'})
X = train_df.drop(columns='target')
y = train_df['target']
print('Shape of y', y.shape) | code |
122261632/cell_62 | [
"text_plain_output_1.png"
] | print('Shape of X_train', X_train.shape) | code |
122261632/cell_59 | [
"text_plain_output_1.png"
] | from imblearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
train_df = pd.merge(train_df_sample, train_label_df, how='inner', on=['customer_ID'])
train_df.drop(axis=1, columns=['customer_ID', 'S_2'], inplace=True)
test_df.drop(axis=1, columns=['S_2'], inplace=True)
i = 0
for col in train_df.columns:
if train_df[col].isnull().sum() / len(train_df[col]) * 100 >= 75:
train_df.drop(labels=col, axis=1, inplace=True)
i = i + 1
i = 0
for col in test_df.columns:
if test_df[col].isnull().sum() / len(test_df[col]) * 100 >= 75:
test_df.drop(labels=col, axis=1, inplace=True)
i = i + 1
train_df = train_df.astype({'B_30': 'str', 'B_38': 'str'})
test_df = test_df.astype({'B_30': 'str', 'B_38': 'str'})
train_df = train_df.astype({'D_114': 'str', 'D_116': 'str', 'D_117': 'str', 'D_120': 'str', 'D_126': 'str', 'D_68': 'str'})
test_df = test_df.astype({'D_114': 'str', 'D_116': 'str', 'D_117': 'str', 'D_120': 'str', 'D_126': 'str', 'D_68': 'str'})
X = train_df.drop(columns='target')
y = train_df['target']
categorical = list(X.select_dtypes('object').columns)
numerical = list(X.select_dtypes('number').columns)
cat_pipe = Pipeline([('imputer', SimpleImputer(strategy='most_frequent', missing_values=np.nan)), ('encoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('scaler', StandardScaler())])
num_pipe = Pipeline([('imputer', SimpleImputer(strategy='most_frequent', missing_values=np.nan)), ('scaler', StandardScaler())])
preprocess = ColumnTransformer([('cat', cat_pipe, categorical), ('num', num_pipe, numerical)])
print(preprocess) | code |
122261632/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
train_df = pd.merge(train_df_sample, train_label_df, how='inner', on=['customer_ID'])
train_df.info() | code |
122261632/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
print('Shape of dataset is:', train_df_sample.shape)
train_df_sample.info() | code |
122261632/cell_75 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
train_df = pd.merge(train_df_sample, train_label_df, how='inner', on=['customer_ID'])
def amex_metric(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:
def top_four_percent_captured(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:
df = pd.concat([y_true, y_pred], axis='columns').sort_values('prediction', ascending=False)
df['weight'] = df['target'].apply(lambda x: 20 if x == 0 else 1)
four_pct_cutoff = int(0.04 * df['weight'].sum())
df['weight_cumsum'] = df['weight'].cumsum()
df_cutoff = df.loc[df['weight_cumsum'] <= four_pct_cutoff]
return (df_cutoff['target'] == 1).sum() / (df['target'] == 1).sum()
def weighted_gini(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:
df = pd.concat([y_true, y_pred], axis='columns').sort_values('prediction', ascending=False)
df['weight'] = df['target'].apply(lambda x: 20 if x == 0 else 1)
df['random'] = (df['weight'] / df['weight'].sum()).cumsum()
total_pos = (df['target'] * df['weight']).sum()
df['cum_pos_found'] = (df['target'] * df['weight']).cumsum()
df['lorentz'] = df['cum_pos_found'] / total_pos
df['gini'] = (df['lorentz'] - df['random']) * df['weight']
return df['gini'].sum()
def normalized_weighted_gini(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:
y_true_pred = y_true.rename(columns={'target': 'prediction'})
return weighted_gini(y_true, y_pred) / weighted_gini(y_true, y_true_pred)
g = normalized_weighted_gini(y_true, y_pred)
d = top_four_percent_captured(y_true, y_pred)
return 0.5 * (g + d)
def model_score(model_name):
model = pipe.fit(X_train, y_train)
model_score('RandomForestClassifier') | code |
122261632/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
test_df.drop(axis=1, columns=['S_2'], inplace=True)
if any(test_df.duplicated()):
print('Yes')
else:
print('No') | code |
122261632/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
train_df = pd.merge(train_df_sample, train_label_df, how='inner', on=['customer_ID'])
train_df.drop(axis=1, columns=['customer_ID', 'S_2'], inplace=True)
i = 0
for col in train_df.columns:
if train_df[col].isnull().sum() / len(train_df[col]) * 100 >= 75:
print('Dropping column', col)
train_df.drop(labels=col, axis=1, inplace=True)
i = i + 1
print('Total number of columns dropped in train dataframe', i) | code |
122261632/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
print('Shape of dataset is:', test_df.shape)
test_df.info() | code |
122261632/cell_53 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
train_df = pd.merge(train_df_sample, train_label_df, how='inner', on=['customer_ID'])
train_df.drop(axis=1, columns=['customer_ID', 'S_2'], inplace=True)
test_df.drop(axis=1, columns=['S_2'], inplace=True)
i = 0
for col in train_df.columns:
if train_df[col].isnull().sum() / len(train_df[col]) * 100 >= 75:
train_df.drop(labels=col, axis=1, inplace=True)
i = i + 1
i = 0
for col in test_df.columns:
if test_df[col].isnull().sum() / len(test_df[col]) * 100 >= 75:
test_df.drop(labels=col, axis=1, inplace=True)
i = i + 1
train_df = train_df.astype({'B_30': 'str', 'B_38': 'str'})
test_df = test_df.astype({'B_30': 'str', 'B_38': 'str'})
train_df = train_df.astype({'D_114': 'str', 'D_116': 'str', 'D_117': 'str', 'D_120': 'str', 'D_126': 'str', 'D_68': 'str'})
test_df = test_df.astype({'D_114': 'str', 'D_116': 'str', 'D_117': 'str', 'D_120': 'str', 'D_126': 'str', 'D_68': 'str'})
X = train_df.drop(columns='target')
y = train_df['target']
categorical = list(X.select_dtypes('object').columns)
print(f'Categorical variables (columns) are: {categorical}')
numerical = list(X.select_dtypes('number').columns)
print(f'Numerical variables (columns) are: {numerical}') | code |
122261632/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df_sample = pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=100000)
train_label_df = pd.read_csv('../input/amex-default-prediction/train_labels.csv')
test_df = pd.read_csv('../input/amex-default-prediction/test_data.csv', nrows=100000, index_col='customer_ID')
train_df = pd.merge(train_df_sample, train_label_df, how='inner', on=['customer_ID'])
train_df.drop(axis=1, columns=['customer_ID', 'S_2'], inplace=True)
if any(train_df.isna().sum()):
print('Yes')
else:
print('No') | code |
88095734/cell_25 | [
"text_html_output_1.png"
] | from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe().columns
df_num = train[['Age', 'SibSp', 'Parch', 'Fare']]
df_cat = train[['Survived', 'Pclass', 'Sex', 'Ticket', 'Cabin', 'Embarked']]
df_cat.Cabin
train['cabin_multiple'] = train.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
train['cabin_multiple'].value_counts()
pd.pivot_table(train, index='Survived', columns='cabin_multiple', values='Ticket', aggfunc='count')
train['cabin_adv'] = train.Cabin.apply(lambda x: str(x)[0])
train['numeric_ticket'] = train.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)
train['ticket_letters'] = train.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.', '').replace('/', '').lower() if len(x.split(' ')[:-1]) > 0 else 0)
train['numeric_ticket'].value_counts()
pd.set_option('max_rows', None)
train['ticket_letters'].value_counts()
pd.pivot_table(train, index='Survived', columns='numeric_ticket', values='Ticket', aggfunc='count')
pd.pivot_table(train, index='Survived', columns='ticket_letters', values='Ticket', aggfunc='count')
train['name_title'] = train.Name.apply(lambda x: x.split(',')[1].split('.')[0].strip())
train['name_title'].value_counts()
data['cabin_multiple'] = data.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
data['cabin_adv'] = data.Cabin.apply(lambda x: str(x)[0])
data['numeric_ticket'] = data.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)
data['ticket_letters'] = data.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.', '').replace('/', '').lower() if len(x.split(' ')[:-1]) > 0 else 0)
data['name_title'] = data.Name.apply(lambda x: x.split(',')[1].split('.')[0].strip())
data.dropna(subset=['Embarked'], inplace=True)
original_age = data.Age
original_fare = data.Fare
data['norm_sibsp'] = np.log(data.SibSp + 1)
data['norm_fare'] = np.log(data.Fare + 1)
data.Pclass = data.Pclass.astype(str)
all_dummies = pd.get_dummies(data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'norm_fare', 'Embarked', 'cabin_adv', 'cabin_multiple', 'numeric_ticket', 'name_title', 'train_test']])
X_train = all_dummies[all_dummies.train_test == 1].drop(['train_test'], axis=1)
X_test = all_dummies[all_dummies.train_test == 0].drop(['train_test'], axis=1)
y_train = data[data.train_test == 1].Survived
y_train.shape
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
all_dummies_scaled = all_dummies.copy()
all_dummies_scaled[['Age', 'SibSp', 'Parch', 'norm_fare']] = scale.fit_transform(all_dummies_scaled[['Age', 'SibSp', 'Parch', 'norm_fare']])
all_dummies_scaled
X_train_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 1].drop(['train_test'], axis=1)
X_test_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 0].drop(['train_test'], axis=1)
y_train = data[data.train_test == 1].Survived
hgb = HistGradientBoostingClassifier()
cv = cross_val_score(hgb, X_train_scaled, y_train, cv=5)
print(cv)
print(f'{cv.mean()} +/-{cv.std():.2f}') | code |
88095734/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.info() | code |
88095734/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe().columns
df_num = train[['Age', 'SibSp', 'Parch', 'Fare']]
df_cat = train[['Survived', 'Pclass', 'Sex', 'Ticket', 'Cabin', 'Embarked']]
df_cat.Cabin
train['cabin_multiple'] = train.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
train['cabin_multiple'].value_counts()
pd.pivot_table(train, index='Survived', columns='cabin_multiple', values='Ticket', aggfunc='count')
train['cabin_adv'] = train.Cabin.apply(lambda x: str(x)[0])
train['numeric_ticket'] = train.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)
train['ticket_letters'] = train.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.', '').replace('/', '').lower() if len(x.split(' ')[:-1]) > 0 else 0)
train['numeric_ticket'].value_counts()
pd.set_option('max_rows', None)
train['ticket_letters'].value_counts()
pd.pivot_table(train, index='Survived', columns='numeric_ticket', values='Ticket', aggfunc='count')
pd.pivot_table(train, index='Survived', columns='ticket_letters', values='Ticket', aggfunc='count')
train['name_title'] = train.Name.apply(lambda x: x.split(',')[1].split('.')[0].strip())
train['name_title'].value_counts()
data['cabin_multiple'] = data.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
data['cabin_adv'] = data.Cabin.apply(lambda x: str(x)[0])
data['numeric_ticket'] = data.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)
data['ticket_letters'] = data.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.', '').replace('/', '').lower() if len(x.split(' ')[:-1]) > 0 else 0)
data['name_title'] = data.Name.apply(lambda x: x.split(',')[1].split('.')[0].strip())
data.dropna(subset=['Embarked'], inplace=True)
original_age = data.Age
original_fare = data.Fare
data['norm_sibsp'] = np.log(data.SibSp + 1)
data['norm_sibsp'].hist()
data['norm_fare'] = np.log(data.Fare + 1)
data['norm_fare'].hist()
data.Pclass = data.Pclass.astype(str)
all_dummies = pd.get_dummies(data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'norm_fare', 'Embarked', 'cabin_adv', 'cabin_multiple', 'numeric_ticket', 'name_title', 'train_test']])
X_train = all_dummies[all_dummies.train_test == 1].drop(['train_test'], axis=1)
X_test = all_dummies[all_dummies.train_test == 0].drop(['train_test'], axis=1)
y_train = data[data.train_test == 1].Survived
y_train.shape | code |
88095734/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe().columns | code |
88095734/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe().columns
df_num = train[['Age', 'SibSp', 'Parch', 'Fare']]
df_cat = train[['Survived', 'Pclass', 'Sex', 'Ticket', 'Cabin', 'Embarked']]
df_cat.Cabin
train['cabin_multiple'] = train.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
train['cabin_multiple'].value_counts()
pd.pivot_table(train, index='Survived', columns='cabin_multiple', values='Ticket', aggfunc='count') | code |
88095734/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88095734/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe().columns
df_num = train[['Age', 'SibSp', 'Parch', 'Fare']]
df_cat = train[['Survived', 'Pclass', 'Sex', 'Ticket', 'Cabin', 'Embarked']]
df_cat.Cabin
train['cabin_multiple'] = train.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
train['cabin_multiple'].value_counts()
train['cabin_adv'] = train.Cabin.apply(lambda x: str(x)[0])
train['numeric_ticket'] = train.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)
train['ticket_letters'] = train.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.', '').replace('/', '').lower() if len(x.split(' ')[:-1]) > 0 else 0)
train['numeric_ticket'].value_counts()
train.Name.head(50)
train['name_title'] = train.Name.apply(lambda x: x.split(',')[1].split('.')[0].strip())
train['name_title'].value_counts() | code |
88095734/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe().columns
df_num = train[['Age', 'SibSp', 'Parch', 'Fare']]
df_cat = train[['Survived', 'Pclass', 'Sex', 'Ticket', 'Cabin', 'Embarked']]
df_cat.Cabin
train['cabin_multiple'] = train.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
train['cabin_multiple'].value_counts()
pd.pivot_table(train, index='Survived', columns='cabin_multiple', values='Ticket', aggfunc='count')
train['cabin_adv'] = train.Cabin.apply(lambda x: str(x)[0])
train['numeric_ticket'] = train.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)
train['ticket_letters'] = train.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.', '').replace('/', '').lower() if len(x.split(' ')[:-1]) > 0 else 0)
train['numeric_ticket'].value_counts()
pd.set_option('max_rows', None)
train['ticket_letters'].value_counts() | code |
88095734/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe().columns
df_num = train[['Age', 'SibSp', 'Parch', 'Fare']]
df_cat = train[['Survived', 'Pclass', 'Sex', 'Ticket', 'Cabin', 'Embarked']]
df_cat.Cabin
train['cabin_multiple'] = train.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
train['cabin_multiple'].value_counts()
pd.pivot_table(train, index='Survived', columns='cabin_multiple', values='Ticket', aggfunc='count')
train['cabin_adv'] = train.Cabin.apply(lambda x: str(x)[0])
train['numeric_ticket'] = train.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)
train['ticket_letters'] = train.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.', '').replace('/', '').lower() if len(x.split(' ')[:-1]) > 0 else 0)
train['numeric_ticket'].value_counts()
pd.set_option('max_rows', None)
train['ticket_letters'].value_counts()
pd.pivot_table(train, index='Survived', columns='numeric_ticket', values='Ticket', aggfunc='count') | code |
88095734/cell_3 | [
"text_plain_output_1.png"
] | train['train_test'] = 1
test['train_test'] = 0
test['Survived'] = np.NaN
data = pd.concat([train, test])
data.columns | code |
88095734/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe().columns
df_num = train[['Age', 'SibSp', 'Parch', 'Fare']]
df_cat = train[['Survived', 'Pclass', 'Sex', 'Ticket', 'Cabin', 'Embarked']]
df_cat.Cabin
train['cabin_multiple'] = train.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
train['cabin_multiple'].value_counts()
pd.pivot_table(train, index='Survived', columns='cabin_multiple', values='Ticket', aggfunc='count')
train['cabin_adv'] = train.Cabin.apply(lambda x: str(x)[0])
train['numeric_ticket'] = train.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)
train['ticket_letters'] = train.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.', '').replace('/', '').lower() if len(x.split(' ')[:-1]) > 0 else 0)
train['numeric_ticket'].value_counts()
pd.set_option('max_rows', None)
train['ticket_letters'].value_counts()
pd.pivot_table(train, index='Survived', columns='numeric_ticket', values='Ticket', aggfunc='count')
pd.pivot_table(train, index='Survived', columns='ticket_letters', values='Ticket', aggfunc='count') | code |
88095734/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe().columns
df_num = train[['Age', 'SibSp', 'Parch', 'Fare']]
df_cat = train[['Survived', 'Pclass', 'Sex', 'Ticket', 'Cabin', 'Embarked']]
df_cat.Cabin
train['cabin_multiple'] = train.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
train['cabin_multiple'].value_counts()
train['cabin_adv'] = train.Cabin.apply(lambda x: str(x)[0])
train['numeric_ticket'] = train.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)
train['ticket_letters'] = train.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.', '').replace('/', '').lower() if len(x.split(' ')[:-1]) > 0 else 0)
train['numeric_ticket'].value_counts() | code |
88095734/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe().columns
df_num = train[['Age', 'SibSp', 'Parch', 'Fare']]
df_cat = train[['Survived', 'Pclass', 'Sex', 'Ticket', 'Cabin', 'Embarked']]
df_cat.Cabin
train['cabin_multiple'] = train.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))
train['cabin_multiple'].value_counts() | code |
88095734/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe() | code |
128029410/cell_4 | [
"text_plain_output_1.png"
] | !pip --version | code |
128029410/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from monai.config import print_config
import os
import json
import shutil
import tempfile
import time
import matplotlib.pyplot as plt
import numpy as np
import nibabel as nib
from monai.losses import DiceLoss
from monai.inferers import sliding_window_inference
from monai import transforms
from monai.transforms import AsDiscrete, Activations
from monai.config import print_config
from monai.metrics import DiceMetric
from monai.utils.enums import MetricReduction
from monai.networks.nets import SwinUNETR
from monai import data
from monai.data import decollate_batch
from functools import partial
import torch
print_config() | code |
128029410/cell_2 | [
"text_plain_output_1.png"
] | !nvidia-smi | code |
128029410/cell_18 | [
"text_plain_output_1.png"
] | from monai import data
from monai import transforms
import json
import matplotlib.pyplot as plt
import nibabel as nib
import numpy as np
import os
import tempfile
import torch
directory = os.environ.get('MONAI_DATA_DIRECTORY')
root_dir = tempfile.mkdtemp() if directory is None else directory
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = np.where(self.count > 0, self.sum / self.count, self.sum)
def datafold_read(datalist, basedir, fold=0, key='training'):
with open(datalist) as f:
json_data = json.load(f)
json_data = json_data[key]
for d in json_data:
for k, v in d.items():
if isinstance(d[k], list):
d[k] = [os.path.join(basedir, iv) for iv in d[k]]
elif isinstance(d[k], str):
d[k] = os.path.join(basedir, d[k]) if len(d[k]) > 0 else d[k]
tr = []
val = []
for d in json_data:
if 'fold' in d and d['fold'] == fold:
val.append(d)
else:
tr.append(d)
return (tr, val)
def save_checkpoint(model, epoch, filename='model.pt', best_acc=0, dir_add=root_dir):
state_dict = model.state_dict()
save_dict = {'epoch': epoch, 'best_acc': best_acc, 'state_dict': state_dict}
filename = os.path.join(dir_add, filename)
torch.save(save_dict, filename)
def get_loader(batch_size, data_dir, json_list, fold, roi):
data_dir = data_dir
datalist_json = json_list
train_files, validation_files = datafold_read(datalist=datalist_json, basedir=data_dir, fold=fold)
train_transform = transforms.Compose([transforms.LoadImaged(keys=['image', 'label']), transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys='label'), transforms.CropForegroundd(keys=['image', 'label'], source_key='image', k_divisible=[roi[0], roi[1], roi[2]]), transforms.RandSpatialCropd(keys=['image', 'label'], roi_size=[roi[0], roi[1], roi[2]], random_size=False), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=0), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=1), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=2), transforms.NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True), transforms.RandScaleIntensityd(keys='image', factors=0.1, prob=1.0), transforms.RandShiftIntensityd(keys='image', offsets=0.1, prob=1.0)])
val_transform = transforms.Compose([transforms.LoadImaged(keys=['image', 'label']), transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys='label'), transforms.NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True)])
train_ds = data.Dataset(data=train_files, transform=train_transform)
train_loader = data.DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
val_ds = data.Dataset(data=validation_files, transform=val_transform)
val_loader = data.DataLoader(val_ds, batch_size=1, shuffle=False, num_workers=4, pin_memory=True)
return (train_loader, val_loader)
data_dir = '/kaggle/input/brats2021-training-data-son/BraTS2021_Training_Data_Son'
json_list = '/kaggle/input/dataSwinUnet/brats21_folds_d.json'
roi = (128, 128, 128)
batch_size = 1
sw_batch_size = 1
fold = 4
infer_overlap = 0.5
max_epochs = 150
val_every = 10
train_loader, val_loader = get_loader(batch_size, data_dir, json_list, fold, roi)
img_add = os.path.join(data_dir, 'BraTS2021_00006/BraTS2021_00006_flair.nii')
label_add = os.path.join(data_dir, 'BraTS2021_00006/BraTS2021_00006_seg.nii')
img = nib.load(img_add).get_fdata()
label = nib.load(label_add).get_fdata()
print(img.shape, label.shape)
print(f'image shape: {img.shape}, label shape: {label.shape}')
plt.figure('image', (18, 6))
plt.subplot(1, 2, 1)
plt.title('image')
plt.imshow(img[:, :, 78], cmap='gray')
plt.subplot(1, 2, 2)
plt.title('label')
plt.imshow(label[:, :, 78])
plt.show() | code |
128029410/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import os
import tempfile
directory = os.environ.get('MONAI_DATA_DIRECTORY')
root_dir = tempfile.mkdtemp() if directory is None else directory
print(root_dir) | code |
128029410/cell_15 | [
"text_plain_output_1.png"
] | from monai import data
from monai import transforms
import json
import numpy as np
import os
import tempfile
import torch
directory = os.environ.get('MONAI_DATA_DIRECTORY')
root_dir = tempfile.mkdtemp() if directory is None else directory
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = np.where(self.count > 0, self.sum / self.count, self.sum)
def datafold_read(datalist, basedir, fold=0, key='training'):
with open(datalist) as f:
json_data = json.load(f)
json_data = json_data[key]
for d in json_data:
for k, v in d.items():
if isinstance(d[k], list):
d[k] = [os.path.join(basedir, iv) for iv in d[k]]
elif isinstance(d[k], str):
d[k] = os.path.join(basedir, d[k]) if len(d[k]) > 0 else d[k]
tr = []
val = []
for d in json_data:
if 'fold' in d and d['fold'] == fold:
val.append(d)
else:
tr.append(d)
return (tr, val)
def save_checkpoint(model, epoch, filename='model.pt', best_acc=0, dir_add=root_dir):
state_dict = model.state_dict()
save_dict = {'epoch': epoch, 'best_acc': best_acc, 'state_dict': state_dict}
filename = os.path.join(dir_add, filename)
torch.save(save_dict, filename)
def get_loader(batch_size, data_dir, json_list, fold, roi):
data_dir = data_dir
datalist_json = json_list
train_files, validation_files = datafold_read(datalist=datalist_json, basedir=data_dir, fold=fold)
train_transform = transforms.Compose([transforms.LoadImaged(keys=['image', 'label']), transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys='label'), transforms.CropForegroundd(keys=['image', 'label'], source_key='image', k_divisible=[roi[0], roi[1], roi[2]]), transforms.RandSpatialCropd(keys=['image', 'label'], roi_size=[roi[0], roi[1], roi[2]], random_size=False), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=0), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=1), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=2), transforms.NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True), transforms.RandScaleIntensityd(keys='image', factors=0.1, prob=1.0), transforms.RandShiftIntensityd(keys='image', offsets=0.1, prob=1.0)])
val_transform = transforms.Compose([transforms.LoadImaged(keys=['image', 'label']), transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys='label'), transforms.NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True)])
train_ds = data.Dataset(data=train_files, transform=train_transform)
train_loader = data.DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
val_ds = data.Dataset(data=validation_files, transform=val_transform)
val_loader = data.DataLoader(val_ds, batch_size=1, shuffle=False, num_workers=4, pin_memory=True)
return (train_loader, val_loader)
print(train_loader, val_loader) | code |
128029410/cell_16 | [
"text_plain_output_1.png"
] | from monai import data
from monai import transforms
import json
import numpy as np
import os
import tempfile
import torch
directory = os.environ.get('MONAI_DATA_DIRECTORY')
root_dir = tempfile.mkdtemp() if directory is None else directory
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = np.where(self.count > 0, self.sum / self.count, self.sum)
def datafold_read(datalist, basedir, fold=0, key='training'):
with open(datalist) as f:
json_data = json.load(f)
json_data = json_data[key]
for d in json_data:
for k, v in d.items():
if isinstance(d[k], list):
d[k] = [os.path.join(basedir, iv) for iv in d[k]]
elif isinstance(d[k], str):
d[k] = os.path.join(basedir, d[k]) if len(d[k]) > 0 else d[k]
tr = []
val = []
for d in json_data:
if 'fold' in d and d['fold'] == fold:
val.append(d)
else:
tr.append(d)
return (tr, val)
def save_checkpoint(model, epoch, filename='model.pt', best_acc=0, dir_add=root_dir):
state_dict = model.state_dict()
save_dict = {'epoch': epoch, 'best_acc': best_acc, 'state_dict': state_dict}
filename = os.path.join(dir_add, filename)
torch.save(save_dict, filename)
def get_loader(batch_size, data_dir, json_list, fold, roi):
data_dir = data_dir
datalist_json = json_list
train_files, validation_files = datafold_read(datalist=datalist_json, basedir=data_dir, fold=fold)
train_transform = transforms.Compose([transforms.LoadImaged(keys=['image', 'label']), transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys='label'), transforms.CropForegroundd(keys=['image', 'label'], source_key='image', k_divisible=[roi[0], roi[1], roi[2]]), transforms.RandSpatialCropd(keys=['image', 'label'], roi_size=[roi[0], roi[1], roi[2]], random_size=False), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=0), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=1), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=2), transforms.NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True), transforms.RandScaleIntensityd(keys='image', factors=0.1, prob=1.0), transforms.RandShiftIntensityd(keys='image', offsets=0.1, prob=1.0)])
val_transform = transforms.Compose([transforms.LoadImaged(keys=['image', 'label']), transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys='label'), transforms.NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True)])
train_ds = data.Dataset(data=train_files, transform=train_transform)
train_loader = data.DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
val_ds = data.Dataset(data=validation_files, transform=val_transform)
val_loader = data.DataLoader(val_ds, batch_size=1, shuffle=False, num_workers=4, pin_memory=True)
return (train_loader, val_loader)
x = next(iter(train_loader))
y = x.get(0)
print(f'y shape={type(y)} dtype={y}') | code |
128029410/cell_3 | [
"text_plain_output_1.png"
] | !pip install "monai[einops]" | code |
128029410/cell_14 | [
"text_plain_output_1.png"
] | from monai import data
from monai import transforms
import json
import numpy as np
import os
import tempfile
import torch
directory = os.environ.get('MONAI_DATA_DIRECTORY')
root_dir = tempfile.mkdtemp() if directory is None else directory
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = np.where(self.count > 0, self.sum / self.count, self.sum)
def datafold_read(datalist, basedir, fold=0, key='training'):
with open(datalist) as f:
json_data = json.load(f)
json_data = json_data[key]
for d in json_data:
for k, v in d.items():
if isinstance(d[k], list):
d[k] = [os.path.join(basedir, iv) for iv in d[k]]
elif isinstance(d[k], str):
d[k] = os.path.join(basedir, d[k]) if len(d[k]) > 0 else d[k]
tr = []
val = []
for d in json_data:
if 'fold' in d and d['fold'] == fold:
val.append(d)
else:
tr.append(d)
return (tr, val)
def save_checkpoint(model, epoch, filename='model.pt', best_acc=0, dir_add=root_dir):
state_dict = model.state_dict()
save_dict = {'epoch': epoch, 'best_acc': best_acc, 'state_dict': state_dict}
filename = os.path.join(dir_add, filename)
torch.save(save_dict, filename)
def get_loader(batch_size, data_dir, json_list, fold, roi):
data_dir = data_dir
datalist_json = json_list
train_files, validation_files = datafold_read(datalist=datalist_json, basedir=data_dir, fold=fold)
train_transform = transforms.Compose([transforms.LoadImaged(keys=['image', 'label']), transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys='label'), transforms.CropForegroundd(keys=['image', 'label'], source_key='image', k_divisible=[roi[0], roi[1], roi[2]]), transforms.RandSpatialCropd(keys=['image', 'label'], roi_size=[roi[0], roi[1], roi[2]], random_size=False), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=0), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=1), transforms.RandFlipd(keys=['image', 'label'], prob=0.5, spatial_axis=2), transforms.NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True), transforms.RandScaleIntensityd(keys='image', factors=0.1, prob=1.0), transforms.RandShiftIntensityd(keys='image', offsets=0.1, prob=1.0)])
val_transform = transforms.Compose([transforms.LoadImaged(keys=['image', 'label']), transforms.ConvertToMultiChannelBasedOnBratsClassesd(keys='label'), transforms.NormalizeIntensityd(keys='image', nonzero=True, channel_wise=True)])
train_ds = data.Dataset(data=train_files, transform=train_transform)
train_loader = data.DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
val_ds = data.Dataset(data=validation_files, transform=val_transform)
val_loader = data.DataLoader(val_ds, batch_size=1, shuffle=False, num_workers=4, pin_memory=True)
return (train_loader, val_loader)
data_dir = '/kaggle/input/brats2021-training-data-son/BraTS2021_Training_Data_Son'
json_list = '/kaggle/input/dataSwinUnet/brats21_folds_d.json'
roi = (128, 128, 128)
batch_size = 1
sw_batch_size = 1
fold = 4
infer_overlap = 0.5
max_epochs = 150
val_every = 10
train_loader, val_loader = get_loader(batch_size, data_dir, json_list, fold, roi) | code |
18149087/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np, pandas as pd, os
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.model_selection import StratifiedKFold
from sklearn.feature_selection import VarianceThreshold
from tqdm import tqdm
from sklearn.covariance import EmpiricalCovariance
from sklearn.covariance import GraphicalLasso
from sklearn.metrics import roc_auc_score
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.mixture import GaussianMixture
from sklearn.svm import NuSVC
from sklearn import svm, neighbors, linear_model, neural_network
from sklearn.ensemble import RandomForestClassifier
from tqdm import tqdm_notebook
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
18149087/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.covariance import GraphicalLasso
from sklearn.feature_selection import VarianceThreshold
from sklearn.metrics import roc_auc_score
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import StratifiedKFold
from tqdm import tqdm
import numpy as np, pandas as pd, os
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.model_selection import StratifiedKFold
from sklearn.feature_selection import VarianceThreshold
from tqdm import tqdm
from sklearn.covariance import EmpiricalCovariance
from sklearn.covariance import GraphicalLasso
from sklearn.metrics import roc_auc_score
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.mixture import GaussianMixture
from sklearn.svm import NuSVC
from sklearn import svm, neighbors, linear_model, neural_network
from sklearn.ensemble import RandomForestClassifier
from tqdm import tqdm_notebook
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def get_mean_cov(x, y):
model = GraphicalLasso()
ones = (y == 1).astype(bool)
x2 = x[ones]
model.fit(x2)
p1 = model.precision_
m1 = model.location_
onesb = (y == 0).astype(bool)
x2b = x[onesb]
model.fit(x2b)
p2 = model.precision_
m2 = model.location_
ms = np.stack([m1, m2])
ps = np.stack([p1, p2])
return (ms, ps)
cols = [c for c in train.columns if c not in ['id', 'target']]
cols.remove('wheezy-copper-turtle-magic')
oof = np.zeros(len(train))
preds = np.zeros(len(test))
for i in tqdm(range(512)):
train2 = train[train['wheezy-copper-turtle-magic'] == i]
test2 = test[test['wheezy-copper-turtle-magic'] == i]
idx1 = train2.index
idx2 = test2.index
train2.reset_index(drop=True, inplace=True)
sel = VarianceThreshold(threshold=1.5).fit(train2[cols])
train3 = sel.transform(train2[cols])
test3 = sel.transform(test2[cols])
skf = StratifiedKFold(n_splits=11, random_state=42, shuffle=True)
for train_index, test_index in skf.split(train3, train2['target']):
ms, ps = get_mean_cov(train3[train_index, :], train2.loc[train_index]['target'].values)
gm = GaussianMixture(n_components=2, init_params='random', covariance_type='full', tol=0.001, reg_covar=0.001, max_iter=100, n_init=1, means_init=ms, precisions_init=ps)
gm.fit(np.concatenate([train3, test3], axis=0))
oof[idx1[test_index]] = gm.predict_proba(train3[test_index, :])[:, 0]
preds[idx2] += gm.predict_proba(test3)[:, 0] / skf.n_splits
auc = roc_auc_score(train['target'], oof)
print('QDA scores CV =', round(auc, 5)) | code |
18149087/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.covariance import GraphicalLasso
from sklearn.feature_selection import VarianceThreshold
from sklearn.metrics import roc_auc_score
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import StratifiedKFold
from tqdm import tqdm
import numpy as np, pandas as pd, os
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.model_selection import StratifiedKFold
from sklearn.feature_selection import VarianceThreshold
from tqdm import tqdm
from sklearn.covariance import EmpiricalCovariance
from sklearn.covariance import GraphicalLasso
from sklearn.metrics import roc_auc_score
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.mixture import GaussianMixture
from sklearn.svm import NuSVC
from sklearn import svm, neighbors, linear_model, neural_network
from sklearn.ensemble import RandomForestClassifier
from tqdm import tqdm_notebook
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def get_mean_cov(x, y):
model = GraphicalLasso()
ones = (y == 1).astype(bool)
x2 = x[ones]
model.fit(x2)
p1 = model.precision_
m1 = model.location_
onesb = (y == 0).astype(bool)
x2b = x[onesb]
model.fit(x2b)
p2 = model.precision_
m2 = model.location_
ms = np.stack([m1, m2])
ps = np.stack([p1, p2])
return (ms, ps)
cols = [c for c in train.columns if c not in ['id', 'target']]
cols.remove('wheezy-copper-turtle-magic')
oof = np.zeros(len(train))
preds = np.zeros(len(test))
for i in tqdm(range(512)):
train2 = train[train['wheezy-copper-turtle-magic'] == i]
test2 = test[test['wheezy-copper-turtle-magic'] == i]
idx1 = train2.index
idx2 = test2.index
train2.reset_index(drop=True, inplace=True)
sel = VarianceThreshold(threshold=1.5).fit(train2[cols])
train3 = sel.transform(train2[cols])
test3 = sel.transform(test2[cols])
skf = StratifiedKFold(n_splits=11, random_state=42, shuffle=True)
for train_index, test_index in skf.split(train3, train2['target']):
ms, ps = get_mean_cov(train3[train_index, :], train2.loc[train_index]['target'].values)
gm = GaussianMixture(n_components=2, init_params='random', covariance_type='full', tol=0.001, reg_covar=0.001, max_iter=100, n_init=1, means_init=ms, precisions_init=ps)
gm.fit(np.concatenate([train3, test3], axis=0))
oof[idx1[test_index]] = gm.predict_proba(train3[test_index, :])[:, 0]
preds[idx2] += gm.predict_proba(test3)[:, 0] / skf.n_splits
auc = roc_auc_score(train['target'], oof)
cat_dict = dict()
cols = [c for c in train.columns if c not in ['id', 'target']]
cols.remove('wheezy-copper-turtle-magic')
for i in range(512):
train2 = train[train['wheezy-copper-turtle-magic'] == i]
test2 = test[test['wheezy-copper-turtle-magic'] == i]
idx1 = train2.index
idx2 = test2.index
train2.reset_index(drop=True, inplace=True)
sel = VarianceThreshold(threshold=1.5).fit(train2[cols])
train3 = sel.transform(train2[cols])
test3 = sel.transform(test2[cols])
cat_dict[i] = train3.shape[1]
pd.DataFrame(list(cat_dict.items()))[1].value_counts().plot.barh() | code |
18149087/cell_10 | [
"text_html_output_1.png"
] | from sklearn import svm, neighbors, linear_model, neural_network
from sklearn.covariance import GraphicalLasso
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import VarianceThreshold
from sklearn.metrics import roc_auc_score
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import StratifiedKFold
from sklearn.svm import NuSVC
from tqdm import tqdm
import numpy as np, pandas as pd, os
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.model_selection import StratifiedKFold
from sklearn.feature_selection import VarianceThreshold
from tqdm import tqdm
from sklearn.covariance import EmpiricalCovariance
from sklearn.covariance import GraphicalLasso
from sklearn.metrics import roc_auc_score
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.mixture import GaussianMixture
from sklearn.svm import NuSVC
from sklearn import svm, neighbors, linear_model, neural_network
from sklearn.ensemble import RandomForestClassifier
from tqdm import tqdm_notebook
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def get_mean_cov(x, y):
model = GraphicalLasso()
ones = (y == 1).astype(bool)
x2 = x[ones]
model.fit(x2)
p1 = model.precision_
m1 = model.location_
onesb = (y == 0).astype(bool)
x2b = x[onesb]
model.fit(x2b)
p2 = model.precision_
m2 = model.location_
ms = np.stack([m1, m2])
ps = np.stack([p1, p2])
return (ms, ps)
cols = [c for c in train.columns if c not in ['id', 'target']]
cols.remove('wheezy-copper-turtle-magic')
oof = np.zeros(len(train))
preds = np.zeros(len(test))
for i in tqdm(range(512)):
train2 = train[train['wheezy-copper-turtle-magic'] == i]
test2 = test[test['wheezy-copper-turtle-magic'] == i]
idx1 = train2.index
idx2 = test2.index
train2.reset_index(drop=True, inplace=True)
sel = VarianceThreshold(threshold=1.5).fit(train2[cols])
train3 = sel.transform(train2[cols])
test3 = sel.transform(test2[cols])
skf = StratifiedKFold(n_splits=11, random_state=42, shuffle=True)
for train_index, test_index in skf.split(train3, train2['target']):
ms, ps = get_mean_cov(train3[train_index, :], train2.loc[train_index]['target'].values)
gm = GaussianMixture(n_components=2, init_params='random', covariance_type='full', tol=0.001, reg_covar=0.001, max_iter=100, n_init=1, means_init=ms, precisions_init=ps)
gm.fit(np.concatenate([train3, test3], axis=0))
oof[idx1[test_index]] = gm.predict_proba(train3[test_index, :])[:, 0]
preds[idx2] += gm.predict_proba(test3)[:, 0] / skf.n_splits
auc = roc_auc_score(train['target'], oof)
cat_dict = dict()
cols = [c for c in train.columns if c not in ['id', 'target']]
cols.remove('wheezy-copper-turtle-magic')
for i in range(512):
train2 = train[train['wheezy-copper-turtle-magic'] == i]
test2 = test[test['wheezy-copper-turtle-magic'] == i]
idx1 = train2.index
idx2 = test2.index
train2.reset_index(drop=True, inplace=True)
sel = VarianceThreshold(threshold=1.5).fit(train2[cols])
train3 = sel.transform(train2[cols])
test3 = sel.transform(test2[cols])
cat_dict[i] = train3.shape[1]
pd.DataFrame(list(cat_dict.items()))[1].value_counts().plot.barh()
test['target'] = preds
oof_qda = np.zeros(len(train))
preds_qda = np.zeros(len(test))
oof_knn = np.zeros(len(train))
preds_knn = np.zeros(len(test))
oof_svnu = np.zeros(len(train))
preds_svnu = np.zeros(len(test))
oof_svc = np.zeros(len(train))
preds_svc = np.zeros(len(test))
oof_rf = np.zeros(len(train))
preds_rf = np.zeros(len(test))
oof_mlp = np.zeros(len(train))
preds_mlp = np.zeros(len(test))
for k in range(512):
train2 = train[train['wheezy-copper-turtle-magic'] == k]
train2p = train2.copy()
idx1 = train2.index
test2 = test[test['wheezy-copper-turtle-magic'] == k]
test2p = test2[(test2['target'] <= 0.01) | (test2['target'] >= 0.99)].copy()
test2p.loc[test2p['target'] >= 0.5, 'target'] = 1
test2p.loc[test2p['target'] < 0.5, 'target'] = 0
train2p = pd.concat([train2p, test2p], axis=0)
train2p.reset_index(drop=True, inplace=True)
pca = PCA(n_components=cat_dict[k], random_state=1234)
pca.fit(train2p[cols])
train3p = pca.transform(train2p[cols])
train3 = pca.transform(train2[cols])
test3 = pca.transform(test2[cols])
skf = StratifiedKFold(n_splits=11, random_state=42, shuffle=True)
for train_index, test_index in skf.split(train3p, train2p['target']):
test_index3 = test_index[test_index < len(train3)]
clf = QuadraticDiscriminantAnalysis(reg_param=0.5)
clf.fit(train3p[train_index, :], train2p.loc[train_index]['target'])
oof_qda[idx1[test_index3]] = clf.predict_proba(train3[test_index3, :])[:, 1]
preds_qda[test2.index] += clf.predict_proba(test3)[:, 1] / skf.n_splits
clf = neighbors.KNeighborsClassifier(n_neighbors=17, p=2.9)
clf.fit(train3p[train_index, :], train2p.loc[train_index]['target'])
oof_knn[idx1[test_index3]] = clf.predict_proba(train3[test_index3, :])[:, 1]
preds_knn[test2.index] += clf.predict_proba(test3)[:, 1] / skf.n_splits
clf = NuSVC(probability=True, kernel='poly', degree=4, gamma='auto', random_state=4, nu=0.59, coef0=0.053)
clf.fit(train3p[train_index, :], train2p.loc[train_index]['target'])
oof_svnu[idx1[test_index3]] = clf.predict_proba(train3[test_index3, :])[:, 1]
preds_svnu[test2.index] += clf.predict_proba(test3)[:, 1] / skf.n_splits
clf = svm.SVC(probability=True, kernel='poly', degree=4, gamma='auto', random_state=42)
clf.fit(train3p[train_index, :], train2p.loc[train_index]['target'])
oof_svc[idx1[test_index3]] = clf.predict_proba(train3[test_index3, :])[:, 1]
preds_svc[test2.index] += clf.predict_proba(test3)[:, 1] / skf.n_splits
clf = RandomForestClassifier(n_estimators=100, random_state=1)
clf.fit(train3p[train_index, :], train2p.loc[train_index]['target'])
oof_rf[idx1[test_index3]] = clf.predict_proba(train3[test_index3, :])[:, 1]
preds_rf[test2.index] += clf.predict_proba(test3)[:, 1] / skf.n_splits
clf = neural_network.MLPClassifier(random_state=3, activation='relu', solver='lbfgs', tol=1e-06, hidden_layer_sizes=(250,))
clf.fit(train3p[train_index, :], train2p.loc[train_index]['target'])
oof_mlp[idx1[test_index3]] = clf.predict_proba(train3[test_index3, :])[:, 1]
preds_mlp[test2.index] += clf.predict_proba(test3)[:, 1] / skf.n_splits
if k % 32 == 0:
print(k)
auc = roc_auc_score(train['target'], oof_qda)
print('Pseudo Labeled QDA scores CV =', round(auc, 5))
auc = roc_auc_score(train['target'], oof_knn)
print('Pseudo Labeled KNN scores CV =', round(auc, 5))
auc = roc_auc_score(train['target'], oof_svnu)
print('Pseudo Labeled SVNU scores CV =', round(auc, 5))
auc = roc_auc_score(train['target'], oof_svc)
print('Pseudo Labeled SVC scores CV =', round(auc, 5))
auc = roc_auc_score(train['target'], oof_rf)
print('Pseudo Labeled RF scores CV =', round(auc, 5))
auc = roc_auc_score(train['target'], oof_mlp)
print('Pseudo Labeled MLP scores CV =', round(auc, 5)) | code |
2003574/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(train_X, train_y)
from sklearn.ensemble import GradientBoostingClassifier
clf = GradientBoostingClassifier()
clf.fit(train_X, train_y)
print('The training score is: {}\n'.format(clf.score(train_X, train_y)))
print('The test score is: {}\n'.format(clf.score(test_X, test_y))) | code |
2003574/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(train_X, train_y)
from sklearn.ensemble import GradientBoostingClassifier
clf = GradientBoostingClassifier()
clf.fit(train_X, train_y)
from sklearn.svm import SVC
clf = SVC(gamma=1)
clf.fit(train_X, train_y)
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(train_X, train_y)
print('The training score is: {:.3f}\n'.format(clf.score(train_X, train_y)))
print('The test score is: {:.3f}\n'.format(clf.score(test_X, test_y))) | code |
2003574/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2003574/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(train_X, train_y)
print('The training score is: {}\n'.format(clf.score(train_X, train_y)))
print('The test score is: {}\n'.format(clf.score(test_X, test_y))) | code |
2003574/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(train_X, train_y)
from sklearn.ensemble import GradientBoostingClassifier
clf = GradientBoostingClassifier()
clf.fit(train_X, train_y)
from sklearn.svm import SVC
clf = SVC(gamma=1)
clf.fit(train_X, train_y)
print('The training score is: {:.3f}\n'.format(clf.score(train_X, train_y)))
print('The test score is: {:.3f}\n'.format(clf.score(test_X, test_y))) | code |
128024415/cell_21 | [
"text_html_output_1.png"
] | from json import loads , dumps
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
price = []
for i in js['Abohar']['restaurants'].keys():
if 'North Indian' in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
print(sum(price) // len(price)) | code |
128024415/cell_25 | [
"text_plain_output_1.png"
] | from json import loads , dumps
import pandas as pd
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
cuisines = []
for i in js['Abohar']['restaurants'].keys():
cuisines += js['Abohar']['restaurants'][i]['cuisine'].split(',')
cuisines = list(set(cuisines))
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df[df['freq'] == max(df['freq'])]
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df.sort_values(by='freq', ascending=False).head(5)
price = []
for i in js['Abohar']['restaurants'].keys():
if 'North Indian' in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine = []
for cuisine in cuisines:
price = []
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine.append([cuisine, sum(price) // len(price)])
avg_cuisine = pd.DataFrame(avg_cuisine, columns=['cuisine', 'avg_cost'])
avg_cuisine.sort_values(by='avg_cost', ascending=False).head() | code |
128024415/cell_23 | [
"text_plain_output_1.png"
] | from json import loads , dumps
import pandas as pd
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
cuisines = []
for i in js['Abohar']['restaurants'].keys():
cuisines += js['Abohar']['restaurants'][i]['cuisine'].split(',')
cuisines = list(set(cuisines))
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df[df['freq'] == max(df['freq'])]
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df.sort_values(by='freq', ascending=False).head(5)
price = []
for i in js['Abohar']['restaurants'].keys():
if 'North Indian' in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine = []
for cuisine in cuisines:
price = []
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine.append([cuisine, sum(price) // len(price)])
avg_cuisine = pd.DataFrame(avg_cuisine, columns=['cuisine', 'avg_cost'])
avg_cuisine.head() | code |
128024415/cell_33 | [
"text_html_output_1.png"
] | from json import loads , dumps
import numpy as np
import pandas as pd
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
cuisines = []
for i in js['Abohar']['restaurants'].keys():
cuisines += js['Abohar']['restaurants'][i]['cuisine'].split(',')
cuisines = list(set(cuisines))
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df[df['freq'] == max(df['freq'])]
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df.sort_values(by='freq', ascending=False).head(5)
price = []
for i in js['Abohar']['restaurants'].keys():
if 'North Indian' in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine = []
for cuisine in cuisines:
price = []
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine.append([cuisine, sum(price) // len(price)])
avg_cuisine = pd.DataFrame(avg_cuisine, columns=['cuisine', 'avg_cost'])
rest_city = []
for i in js.keys():
c = 0
if 'restaurants' in js[i].keys():
c = len(js[i]['restaurants'])
else:
for region in js[i].keys():
if 'restaurants' in js[i][region].keys():
c = len(js[i][region]['restaurants'])
rest_city.append([i, c])
rest_city = pd.DataFrame(rest_city, columns=['city', 'total_restaurants'])
c = 0
arr = []
for city in js.keys():
if 'restaurants' in js[city].keys():
for rest in js[city]['restaurants'].keys():
if 'menu' in js[city]['restaurants'][rest].keys():
if len(js[city]['restaurants'][rest]['menu'].keys()) == 0:
c += 1
arr.append([rest])
else:
c += 1
arr.append([rest])
else:
for regions in js[city].keys():
if 'restaurants' in js[city][regions].keys():
for rest in js[city][regions]['restaurants'].keys():
if 'menu' in js[city][regions]['restaurants'][rest].keys():
if len(js[city][regions]['restaurants'][rest]['menu'].keys()) == 0:
c += 1
arr.append([rest])
else:
c += 1
arr.append([rest])
print(c)
arr = np.array(arr)
np.save('incompleted_rest_data.npy', arr) | code |
128024415/cell_6 | [
"text_html_output_1.png"
] | from json import loads , dumps
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
print(len(js.keys())) | code |
128024415/cell_19 | [
"text_html_output_1.png"
] | from json import loads , dumps
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
cost = []
for i in js['Abohar']['restaurants'].keys():
cost.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cost = round(sum(cost) / len(cost), 2)
print('Average cost of eating outside in abohar is : Rs. ' + str(avg_cost)) | code |
128024415/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
from json import loads, dumps
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128024415/cell_8 | [
"text_html_output_1.png"
] | from json import loads , dumps
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
print(len(js['Abohar']['restaurants'].keys())) | code |
128024415/cell_16 | [
"text_plain_output_1.png"
] | from json import loads , dumps
import pandas as pd
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
cuisines = []
for i in js['Abohar']['restaurants'].keys():
cuisines += js['Abohar']['restaurants'][i]['cuisine'].split(',')
cuisines = list(set(cuisines))
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df[df['freq'] == max(df['freq'])] | code |
128024415/cell_17 | [
"text_plain_output_1.png"
] | from json import loads , dumps
import pandas as pd
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
cuisines = []
for i in js['Abohar']['restaurants'].keys():
cuisines += js['Abohar']['restaurants'][i]['cuisine'].split(',')
cuisines = list(set(cuisines))
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df[df['freq'] == max(df['freq'])]
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df.sort_values(by='freq', ascending=False).head(5) | code |
128024415/cell_35 | [
"text_html_output_1.png"
] | from json import loads , dumps
import numpy as np
import pandas as pd
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
cuisines = []
for i in js['Abohar']['restaurants'].keys():
cuisines += js['Abohar']['restaurants'][i]['cuisine'].split(',')
cuisines = list(set(cuisines))
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df[df['freq'] == max(df['freq'])]
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df.sort_values(by='freq', ascending=False).head(5)
cost = []
for i in js['Abohar']['restaurants'].keys():
cost.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cost = round(sum(cost) / len(cost), 2)
price = []
for i in js['Abohar']['restaurants'].keys():
if 'North Indian' in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine = []
for cuisine in cuisines:
price = []
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine.append([cuisine, sum(price) // len(price)])
avg_cuisine = pd.DataFrame(avg_cuisine, columns=['cuisine', 'avg_cost'])
rest_city = []
for i in js.keys():
c = 0
if 'restaurants' in js[i].keys():
c = len(js[i]['restaurants'])
else:
for region in js[i].keys():
if 'restaurants' in js[i][region].keys():
c = len(js[i][region]['restaurants'])
rest_city.append([i, c])
rest_city = pd.DataFrame(rest_city, columns=['city', 'total_restaurants'])
c = 0
arr = []
for city in js.keys():
if 'restaurants' in js[city].keys():
for rest in js[city]['restaurants'].keys():
if 'menu' in js[city]['restaurants'][rest].keys():
if len(js[city]['restaurants'][rest]['menu'].keys()) == 0:
c += 1
arr.append([rest])
else:
c += 1
arr.append([rest])
else:
for regions in js[city].keys():
if 'restaurants' in js[city][regions].keys():
for rest in js[city][regions]['restaurants'].keys():
if 'menu' in js[city][regions]['restaurants'][rest].keys():
if len(js[city][regions]['restaurants'][rest]['menu'].keys()) == 0:
c += 1
arr.append([rest])
else:
c += 1
arr.append([rest])
arr = np.array(arr)
np.save('incompleted_rest_data.npy', arr)
avg_cost = []
for city in js.keys():
cost = []
if 'restaurants' in js[city].keys():
for rest in js[city]['restaurants'].keys():
try:
cost.append(int(js[city]['restaurants'][rest]['cost'].split(' ')[-1]))
except:
pass
else:
for region in js[city].keys():
if 'restaurants' in js[city][region].keys():
for rest in js[city][region]['restaurants'].keys():
try:
cost.append(int(js[city][region]['restaurants'][rest]['cost'].split(' ')[-1]))
except:
pass
try:
avg_cost.append([city, sum(cost) // len(cost)])
except:
pass
df_ = pd.DataFrame(avg_cost, columns=['city', 'avg_cost'])
df_.sort_values(by='avg_cost', ascending=False).head(5) | code |
128024415/cell_31 | [
"text_html_output_1.png"
] | from json import loads , dumps
import pandas as pd
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
cuisines = []
for i in js['Abohar']['restaurants'].keys():
cuisines += js['Abohar']['restaurants'][i]['cuisine'].split(',')
cuisines = list(set(cuisines))
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df[df['freq'] == max(df['freq'])]
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df.sort_values(by='freq', ascending=False).head(5)
price = []
for i in js['Abohar']['restaurants'].keys():
if 'North Indian' in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine = []
for cuisine in cuisines:
price = []
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine.append([cuisine, sum(price) // len(price)])
avg_cuisine = pd.DataFrame(avg_cuisine, columns=['cuisine', 'avg_cost'])
rest_city = []
for i in js.keys():
c = 0
if 'restaurants' in js[i].keys():
c = len(js[i]['restaurants'])
else:
for region in js[i].keys():
if 'restaurants' in js[i][region].keys():
c = len(js[i][region]['restaurants'])
rest_city.append([i, c])
rest_city = pd.DataFrame(rest_city, columns=['city', 'total_restaurants'])
rest_city.sort_values(by='total_restaurants', ascending=False).head() | code |
128024415/cell_14 | [
"text_plain_output_1.png"
] | from json import loads , dumps
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
cuisines = []
for i in js['Abohar']['restaurants'].keys():
cuisines += js['Abohar']['restaurants'][i]['cuisine'].split(',')
cuisines = list(set(cuisines))
print(len(cuisines)) | code |
128024415/cell_10 | [
"text_plain_output_1.png"
] | from json import loads , dumps
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
for i in js['Abohar']['restaurants'].keys():
print(js['Abohar']['restaurants'][i]['name'], '|', len(js['Abohar']['restaurants'][i]['menu'].keys())) | code |
128024415/cell_27 | [
"text_html_output_1.png"
] | from json import loads , dumps
import pandas as pd
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
cuisines = []
for i in js['Abohar']['restaurants'].keys():
cuisines += js['Abohar']['restaurants'][i]['cuisine'].split(',')
cuisines = list(set(cuisines))
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df[df['freq'] == max(df['freq'])]
pop_cui = []
for cuisine in cuisines:
c = 0
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
c += 1
pop_cui.append([cuisine, c])
df = pd.DataFrame(pop_cui, columns=['item', 'freq'])
df.sort_values(by='freq', ascending=False).head(5)
price = []
for i in js['Abohar']['restaurants'].keys():
if 'North Indian' in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine = []
for cuisine in cuisines:
price = []
for i in js['Abohar']['restaurants'].keys():
if cuisine in js['Abohar']['restaurants'][i]['cuisine']:
price.append(int(js['Abohar']['restaurants'][i]['cost'].split(' ')[-1]))
avg_cuisine.append([cuisine, sum(price) // len(price)])
avg_cuisine = pd.DataFrame(avg_cuisine, columns=['cuisine', 'avg_cost'])
avg_cuisine.sort_values(by='avg_cost', ascending=False).head()
avg_cuisine.sort_values(by='avg_cost', ascending=True).head() | code |
128024415/cell_12 | [
"text_plain_output_1.png"
] | from json import loads , dumps
file = open('/kaggle/input/swiggy-restaurants-dataset/data.json', 'r')
data = file.read()
file.close()
js = loads(data)
for i in js['Abohar']['restaurants'].keys():
if len(js['Abohar']['restaurants'][i]['menu']) == 0:
print(js['Abohar']['restaurants'][i]['name'], '|', i) | code |
105190732/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.dtypes
label = df_train['Survived']
label.unique()
if label.isnull().sum() == 0:
print('No missing values')
else:
print(label.isnull().sum(), 'missing values found in dataset') | code |
105190732/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.dtypes | code |
105190732/cell_30 | [
"text_plain_output_1.png"
] | 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
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.dtypes
df_train['Age_NA'] = np.where(df_train.Age.isnull(), 1, 0)
df_test['Age_NA'] = np.where(df_test.Age.isnull(), 1, 0)
print(df_train['Age_NA'].value_counts())
sns.factorplot('Age_NA', 'Survived', data=df_train) | code |
105190732/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.dtypes
df_train.Age.describe() | code |
105190732/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.dtypes
for column in df_train.columns:
print(column, len(df_train[column].unique())) | code |
105190732/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from scipy import stats
from scipy.cluster import hierarchy as hc
import sklearn
import IPython
import matplotlib.pyplot as plt
from sklearn.model_selection import ParameterGrid
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, mean_squared_error
from sklearn.preprocessing import OneHotEncoder
from pandas.api.types import is_string_dtype, is_numeric_dtype, is_categorical_dtype
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import seaborn as sns
import string
import math
import sys
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105190732/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
print('Train Shape', df_train.shape)
print('Test Shape', df_test.shape) | code |
105190732/cell_32 | [
"text_plain_output_1.png"
] | 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
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.dtypes
df_train['Age_NA'] = np.where(df_train.Age.isnull(), 1, 0)
df_test['Age_NA'] = np.where(df_test.Age.isnull(), 1, 0)
a = sns.FacetGrid(df_train, hue='Survived', aspect=4)
a.map(sns.kdeplot, 'Age', shade=True)
a.set(xlim=(0, df_train['Age'].max()))
a.add_legend()
print('Skew for train data:', df_train.Age.skew()) | code |
105190732/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.head().transpose() | code |
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