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|---|---|---|---|
16146132/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
num_features.remove('PassengerId')
num_features.remove('Survived')
num_features.append('Survived')
num_features | code |
16146132/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
print('{:.2f}% survival rate, {} out of {} not survived'.format(df_train.Survived.sum() / len(df_train) * 100, df_train.Survived.sum(), len(df_train))) | code |
88087082/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
n_features = 300
features = [f'f_{i}' for i in range(n_features)]
train = pd.read_pickle('../input/ubiquant-market-prediction-half-precision-pickle/train.pkl')
plt.figure(figsize=(25, 30))
plt.title('Pearson Correlation', y=1.05, size=15)
sns.heatmap(train[features].loc[:1000].corr(), annot=True) | code |
88087082/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
n_features = 300
features = [f'f_{i}' for i in range(n_features)]
train = pd.read_pickle('../input/ubiquant-market-prediction-half-precision-pickle/train.pkl')
print(train.shape)
train.head() | code |
88087082/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34127846/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
import numpy as np
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
headline.append(item['headline'])
is_sarcastic.append(item['is_sarcastic'])
data_len = len(headline)
train_size = round(data_len * 80 / 100)
train_headline = headline[0:train_size]
test_headline = headline[train_size:]
train_result = is_sarcastic[0:train_size]
test_result = is_sarcastic[train_size:]
token2 = Tokenizer(oov_token='<OOV>')
token2.fit_on_texts(train_headline)
word_index_2 = token2.word_index
train_seq = token2.texts_to_sequences(train_headline)
train_pad = pd(train_seq)
test_seq = token2.texts_to_sequences(test_headline)
test_pad = pd(test_seq)
vocab_size = len(word_index_2) + 1
vocab_size
vocab_size = len(word_index_2) + 1
model = k.Sequential([k.layers.Embedding(vocab_size, 50), k.layers.GlobalAveragePooling1D(), k.layers.Dense(24, activation='relu'), k.layers.Dense(1, activation='sigmoid')])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
train_pad = np.array(train_pad)
train_result = np.array(train_result)
test_pad = np.array(test_pad)
test_result = np.array(test_result)
training = model.fit(train_pad, train_result, epochs=30, validation_data=(test_pad, test_result), verbose=2) | code |
34127846/cell_9 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
headline.append(item['headline'])
is_sarcastic.append(item['is_sarcastic'])
token = Tokenizer(oov_token='<oov>')
token.fit_on_texts(headline)
word_index = token.word_index
len(word_index) | code |
34127846/cell_20 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
import numpy as np
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
headline.append(item['headline'])
is_sarcastic.append(item['is_sarcastic'])
data_len = len(headline)
train_size = round(data_len * 80 / 100)
train_headline = headline[0:train_size]
test_headline = headline[train_size:]
train_result = is_sarcastic[0:train_size]
test_result = is_sarcastic[train_size:]
token2 = Tokenizer(oov_token='<OOV>')
token2.fit_on_texts(train_headline)
word_index_2 = token2.word_index
train_seq = token2.texts_to_sequences(train_headline)
train_pad = pd(train_seq)
test_seq = token2.texts_to_sequences(test_headline)
test_pad = pd(test_seq)
train_pad = np.array(train_pad)
train_result = np.array(train_result)
test_pad = np.array(test_pad)
test_result = np.array(test_result)
type(train_pad) | code |
34127846/cell_6 | [
"text_plain_output_1.png"
] | import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
headline.append(item['headline'])
is_sarcastic.append(item['is_sarcastic'])
print(article_link[3])
print(headline[3])
print(is_sarcastic[3]) | code |
34127846/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
headline.append(item['headline'])
is_sarcastic.append(item['is_sarcastic'])
token = Tokenizer(oov_token='<oov>')
token.fit_on_texts(headline)
word_index = token.word_index
len(word_index)
seq = token.texts_to_sequences(headline)
padded = pd(seq, padding='post')
padded[0]
print(padded.shape) | code |
34127846/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
headline.append(item['headline'])
is_sarcastic.append(item['is_sarcastic'])
data_len = len(headline)
train_size = round(data_len * 80 / 100)
train_headline = headline[0:train_size]
test_headline = headline[train_size:]
train_result = is_sarcastic[0:train_size]
test_result = is_sarcastic[train_size:]
token2 = Tokenizer(oov_token='<OOV>')
token2.fit_on_texts(train_headline)
word_index_2 = token2.word_index
train_seq = token2.texts_to_sequences(train_headline)
train_pad = pd(train_seq)
test_seq = token2.texts_to_sequences(test_headline)
test_pad = pd(test_seq)
vocab_size = len(word_index_2) + 1
vocab_size
vocab_size = len(word_index_2) + 1
model = k.Sequential([k.layers.Embedding(vocab_size, 50), k.layers.GlobalAveragePooling1D(), k.layers.Dense(24, activation='relu'), k.layers.Dense(1, activation='sigmoid')])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary() | code |
34127846/cell_16 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
headline.append(item['headline'])
is_sarcastic.append(item['is_sarcastic'])
data_len = len(headline)
train_size = round(data_len * 80 / 100)
train_headline = headline[0:train_size]
test_headline = headline[train_size:]
train_result = is_sarcastic[0:train_size]
test_result = is_sarcastic[train_size:]
token2 = Tokenizer(oov_token='<OOV>')
token2.fit_on_texts(train_headline)
word_index_2 = token2.word_index
train_seq = token2.texts_to_sequences(train_headline)
train_pad = pd(train_seq)
test_seq = token2.texts_to_sequences(test_headline)
test_pad = pd(test_seq)
vocab_size = len(word_index_2) + 1
vocab_size | code |
34127846/cell_3 | [
"text_plain_output_1.png"
] | import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
dataset[0] | code |
34127846/cell_22 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
import numpy as np
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
headline.append(item['headline'])
is_sarcastic.append(item['is_sarcastic'])
data_len = len(headline)
train_size = round(data_len * 80 / 100)
train_headline = headline[0:train_size]
test_headline = headline[train_size:]
train_result = is_sarcastic[0:train_size]
test_result = is_sarcastic[train_size:]
token2 = Tokenizer(oov_token='<OOV>')
token2.fit_on_texts(train_headline)
word_index_2 = token2.word_index
train_seq = token2.texts_to_sequences(train_headline)
train_pad = pd(train_seq)
test_seq = token2.texts_to_sequences(test_headline)
test_pad = pd(test_seq)
vocab_size = len(word_index_2) + 1
vocab_size
vocab_size = len(word_index_2) + 1
model = k.Sequential([k.layers.Embedding(vocab_size, 50), k.layers.GlobalAveragePooling1D(), k.layers.Dense(24, activation='relu'), k.layers.Dense(1, activation='sigmoid')])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
train_pad = np.array(train_pad)
train_result = np.array(train_result)
test_pad = np.array(test_pad)
test_result = np.array(test_result)
training = model.fit(train_pad, train_result, epochs=30, validation_data=(test_pad, test_result), verbose=2)
sentences = ['Meh, Kind of good', 'Climate is perfect']
sequences = token2.texts_to_sequences(sentences)
latest_padded = pd(sequences)
model.predict(latest_padded) | code |
34127846/cell_10 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
headline.append(item['headline'])
is_sarcastic.append(item['is_sarcastic'])
token = Tokenizer(oov_token='<oov>')
token.fit_on_texts(headline)
word_index = token.word_index
len(word_index)
seq = token.texts_to_sequences(headline)
padded = pd(seq, padding='post')
padded[0] | code |
34127846/cell_12 | [
"text_plain_output_1.png"
] | import json
dataset = []
for line in open('/kaggle/input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', 'r'):
dataset.append(json.loads(line))
json.load
article_link = []
headline = []
is_sarcastic = []
for item in dataset:
article_link.append(item['article_link'])
headline.append(item['headline'])
is_sarcastic.append(item['is_sarcastic'])
data_len = len(headline)
train_size = round(data_len * 80 / 100)
print(train_size) | code |
122258057/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
train_df['Survived'].value_counts(normalize=True).plot(kind='bar', label='Выжившие') | code |
122258057/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.info() | code |
122258057/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.histplot(train_df['Survived']) | code |
122258057/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
pd.plotting.scatter_matrix(train_df[['Age', 'SibSp']], alpha=0.2) | code |
122258057/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
train_df.info() | code |
122258057/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
train_df['Survived'].hist(bins=2) | code |
122258057/cell_48 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.barplot(x='Sex', y='Survived', hue='Embarked', data=train_df)
plt.legend()
plt.xlabel('пол')
plt.ylabel('Доля выживших')
plt.title('Доля выживших для мужчин и женщин в зависимости от порта') | code |
122258057/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.head(5) | code |
122258057/cell_50 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
f, ax = plt.subplots(figsize=(25, 10))
sns.countplot(x='Age', hue='Survived', data=train_df[(train_df['Age'] > 5) & (train_df['Age'] < 30)]) | code |
122258057/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.pairplot(train_df, vars=['Age', 'SibSp']) | code |
122258057/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df.describe(include='int64') | code |
122258057/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.jointplot(x='Age', y='SibSp', data=train_df) | code |
122258057/cell_47 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.barplot(x='Sex', y='Survived', data=train_df)
plt.legend()
plt.xlabel('пол')
plt.ylabel('доля выживших')
plt.title('Соотношение выживших для мужчин и женщин') | code |
122258057/cell_43 | [
"text_plain_output_1.png"
] | """
sns.boxplot(y="Fare", x="Pclass", data=train_df, orient="h");
Такой boxplot получается не очень красивым из-за выбросов.**
Опционально: создайте признак `Fare_no_out` (стоимости без выбросов), в котором исключаются стоимости, отличающиеся от средней по классу более чем на 2 стандартных отклонения.
Важно: надо исключать выбросы именно в зависимости от класса каюты. Иначе исключаться будут только самые большие (1 класс) и малые (3 класс) стоимости.
train_df['Fare_no_out'] = train_df['Fare']
fare_pclass1 = train_df[train_df['Pclass'] == 1]['Fare']
fare_pclass2 = train_df[train_df['Pclass'] == 2]['Fare']
fare_pclass3 = train_df[train_df['Pclass'] == 3]['Fare']
fare_pclass1_no_out = # Ваш код здесь
fare_pclass2_no_out = # Ваш код здесь
fare_pclass3_no_out = # Ваш код здесь
train_df['Fare_no_out'] = fare_pclass1_no_out.append(fare_pclass2_no_out) .append(fare_pclass3_no_out)
#новый box plot
""" | code |
122258057/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
train_df.info() | code |
122258057/cell_46 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.countplot(x='Sex', hue='Survived', data=train_df)
plt.legend()
plt.xlabel('пол')
plt.ylabel('кол-во выживших')
plt.title('Соотношение выживших для мужчин и женщин')
plt.savefig('qwe.png', dpi=300) | code |
122258057/cell_24 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
sns.displot(train_df['Survived']) | code |
122258057/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum() | code |
122258057/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
plt.hist(x=train_df['Survived']) | code |
122258057/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
train_df.plot.scatter(x='Age', y='SibSp') | code |
122258057/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_df = pd.read_csv('../input/titanic/titanic_train.csv', index_col='PassengerId')
train_df.isnull().sum()
train_df = train_df.drop('Cabin', axis=1).dropna()
plt.scatter(train_df['Age'], train_df['SibSp']) | code |
1009955/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
price = pd.read_csv('../input/price.csv')
price_per_sqft = pd.read_csv('../input/pricepersqft.csv')
flat_price = pd.melt(price, id_vars=['City Code', 'City', 'Metro', 'County', 'State', 'Population Rank'])
flat_price.dropna(inplace=True)
top10 = flat_price[flat_price['variable'] == 'January 2017'].sort_values(by=['value'], ascending=False).head(10)
top10['City_State'] = top10['City'] + ' ' + top10['State']
ax = top10[['City_State', 'value']].plot(kind='bar', use_index=False)
ax.set_xticklabels(top10['City_State']) | code |
1009955/cell_20 | [
"text_html_output_1.png"
] | flat_grouped = flat_price_sorted.groupby(['City_State'])
value_diff = flat_grouped['value'].agg({'value': ['first', 'last']})
value_diff['value']['last'] - value_diff['value']['first'] | code |
1009955/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | flat_grouped = flat_price_sorted.groupby(['City_State'])
value_diff = flat_grouped['value'].agg({'value': ['first', 'last']}) | code |
1009955/cell_18 | [
"text_html_output_1.png"
] | flat_grouped = flat_price_sorted.groupby(['City_State']) | code |
1009955/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
price = pd.read_csv('../input/price.csv')
price_per_sqft = pd.read_csv('../input/pricepersqft.csv')
flat_price = pd.melt(price, id_vars=['City Code', 'City', 'Metro', 'County', 'State', 'Population Rank'])
flat_price.head() | code |
1009955/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1009955/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
price = pd.read_csv('../input/price.csv')
price_per_sqft = pd.read_csv('../input/pricepersqft.csv')
flat_price = pd.melt(price, id_vars=['City Code', 'City', 'Metro', 'County', 'State', 'Population Rank'])
flat_price.dropna(inplace=True)
flat_price.sort_values(by=['City Code', 'date']).head(10) | code |
1009955/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
price = pd.read_csv('../input/price.csv')
price_per_sqft = pd.read_csv('../input/pricepersqft.csv')
price.head() | code |
73073936/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
fig, ax = plt.subplots()
ax.scatter(x = training_dataframe['GrLivArea'], y = training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()
#Deleting outliers
training_dataframe= training_dataframe.drop(training_dataframe[(training_dataframe['GrLivArea']>4000) & (training_dataframe['SalePrice']<300000)].index)
#Check the scatterplot again
fig, ax = plt.subplots()
ax.scatter(training_dataframe['GrLivArea'], training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()
training_dataframe['SalePrice'].describe() | code |
73073936/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
fig, ax = plt.subplots()
ax.scatter(x=training_dataframe['GrLivArea'], y=training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show() | code |
73073936/cell_2 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
training_dataframe.head() | code |
73073936/cell_11 | [
"text_html_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
import warnings
import os
import pandas as pd
import numpy as np
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import math
import matplotlib.pyplot as plt
from scipy.stats import skew
import warnings
def ignore_warn(*args, **kwargs):
pass
warnings.warn = ignore_warn
from scipy import stats
from scipy.stats import norm, skew
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
fig, ax = plt.subplots()
ax.scatter(x = training_dataframe['GrLivArea'], y = training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()
#Deleting outliers
training_dataframe= training_dataframe.drop(training_dataframe[(training_dataframe['GrLivArea']>4000) & (training_dataframe['SalePrice']<300000)].index)
#Check the scatterplot again
fig, ax = plt.subplots()
ax.scatter(training_dataframe['GrLivArea'], training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()
sns.distplot(training_dataframe['SalePrice'], fit=norm)
mu, sigma = norm.fit(training_dataframe['SalePrice'])
print('\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma))
plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)], loc='best')
plt.ylabel('Frequency')
plt.title('SalePrice distribution')
fig = plt.figure()
res = stats.probplot(training_dataframe['SalePrice'], plot=plt)
plt.show() | code |
73073936/cell_19 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import warnings
import os
import pandas as pd
import numpy as np
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import math
import matplotlib.pyplot as plt
from scipy.stats import skew
import warnings
def ignore_warn(*args, **kwargs):
pass
warnings.warn = ignore_warn
from scipy import stats
from scipy.stats import norm, skew
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
fig, ax = plt.subplots()
ax.scatter(x = training_dataframe['GrLivArea'], y = training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()
#Deleting outliers
training_dataframe= training_dataframe.drop(training_dataframe[(training_dataframe['GrLivArea']>4000) & (training_dataframe['SalePrice']<300000)].index)
#Check the scatterplot again
fig, ax = plt.subplots()
ax.scatter(training_dataframe['GrLivArea'], training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()
#Plotting histogram
sns.distplot(training_dataframe['SalePrice'] , fit=norm);
# Fitted Parameters used by function
(mu, sigma) = norm.fit(training_dataframe['SalePrice'])
print( '\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma))
#Plotting the distribution
plt.legend(['Normal dist. ($\mu=$ {:.2f} and $\sigma=$ {:.2f} )'.format(mu, sigma)],
loc='best')
plt.ylabel('Frequency')
plt.title('SalePrice distribution')
#Plotting QQ-plot
fig = plt.figure()
res = stats.probplot(training_dataframe['SalePrice'], plot=plt)
plt.show()
#using log1p which applies log(1+x) to all elements of the column
training_dataframe["SalePrice"] = np.log1p(training_dataframe["SalePrice"])
#Checking the new distribution
sns.distplot(training_dataframe['SalePrice'] , fit=norm);
# Geting fitted parameters used by the function
(mu, sigma) = norm.fit(training_dataframe['SalePrice'])
print( '\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma))
#Plotting Distribution
plt.legend(['Normal dist. ($\mu=$ {:.2f} and $\sigma=$ {:.2f} )'.format(mu, sigma)],
loc='best')
plt.ylabel('Frequency')
plt.title('SalePrice distribution')
#Get also the QQ-plot
fig = plt.figure()
res = stats.probplot(training_dataframe['SalePrice'], plot=plt)
plt.show()
corealtion_matrix = training_dataframe.corr()
best_cor_feature = corealtion_matrix.index[abs(corealtion_matrix['SalePrice']) > 0.5]
best_cor_feature
def plotColor(*args):
a = 3
b = int(len(args) / a) + 1
c = 1
for i in args:
plt.axis('off')
plt.text(0, 0.04, i, color='k', fontsize=11)
plt.hlines(0, 0, 10, color=i, linestyles='solid', linewidth=25)
c = c + 1
plt.tight_layout()
return
ntrain = training_dataframe.shape[0]
ntest = testing_dataframe.shape[0]
y_train = training_dataframe.SalePrice.values
print('y_train shape is : {}'.format(y_train.shape))
all_data = pd.concat((training_dataframe, testing_dataframe)).reset_index(drop=True)
all_data.drop(['SalePrice'], axis=1, inplace=True)
print('all_data size is : {}'.format(all_data.shape))
total = all_data.isnull().sum().sort_values(ascending=False)
percent = (all_data.isnull().sum() / all_data.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data.head(30) | code |
73073936/cell_1 | [
"text_plain_output_1.png"
] | import os
import seaborn as sns
import warnings
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import pandas as pd
import numpy as np
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import math
import matplotlib.pyplot as plt
from scipy.stats import skew
import warnings
def ignore_warn(*args, **kwargs):
pass
warnings.warn = ignore_warn
from scipy import stats
from scipy.stats import norm, skew | code |
73073936/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
fig, ax = plt.subplots()
ax.scatter(x = training_dataframe['GrLivArea'], y = training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()
training_dataframe = training_dataframe.drop(training_dataframe[(training_dataframe['GrLivArea'] > 4000) & (training_dataframe['SalePrice'] < 300000)].index)
fig, ax = plt.subplots()
ax.scatter(training_dataframe['GrLivArea'], training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show() | code |
73073936/cell_16 | [
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import warnings
import os
import pandas as pd
import numpy as np
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import math
import matplotlib.pyplot as plt
from scipy.stats import skew
import warnings
def ignore_warn(*args, **kwargs):
pass
warnings.warn = ignore_warn
from scipy import stats
from scipy.stats import norm, skew
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
fig, ax = plt.subplots()
ax.scatter(x = training_dataframe['GrLivArea'], y = training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()
#Deleting outliers
training_dataframe= training_dataframe.drop(training_dataframe[(training_dataframe['GrLivArea']>4000) & (training_dataframe['SalePrice']<300000)].index)
#Check the scatterplot again
fig, ax = plt.subplots()
ax.scatter(training_dataframe['GrLivArea'], training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()
#Plotting histogram
sns.distplot(training_dataframe['SalePrice'] , fit=norm);
# Fitted Parameters used by function
(mu, sigma) = norm.fit(training_dataframe['SalePrice'])
print( '\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma))
#Plotting the distribution
plt.legend(['Normal dist. ($\mu=$ {:.2f} and $\sigma=$ {:.2f} )'.format(mu, sigma)],
loc='best')
plt.ylabel('Frequency')
plt.title('SalePrice distribution')
#Plotting QQ-plot
fig = plt.figure()
res = stats.probplot(training_dataframe['SalePrice'], plot=plt)
plt.show()
#using log1p which applies log(1+x) to all elements of the column
training_dataframe["SalePrice"] = np.log1p(training_dataframe["SalePrice"])
#Checking the new distribution
sns.distplot(training_dataframe['SalePrice'] , fit=norm);
# Geting fitted parameters used by the function
(mu, sigma) = norm.fit(training_dataframe['SalePrice'])
print( '\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma))
#Plotting Distribution
plt.legend(['Normal dist. ($\mu=$ {:.2f} and $\sigma=$ {:.2f} )'.format(mu, sigma)],
loc='best')
plt.ylabel('Frequency')
plt.title('SalePrice distribution')
#Get also the QQ-plot
fig = plt.figure()
res = stats.probplot(training_dataframe['SalePrice'], plot=plt)
plt.show()
corealtion_matrix = training_dataframe.corr()
best_cor_feature = corealtion_matrix.index[abs(corealtion_matrix['SalePrice']) > 0.5]
plt.figure(figsize=(12, 12))
sns.heatmap(training_dataframe[best_cor_feature].corr(), annot=True)
best_cor_feature
def plotColor(*args):
a = 3
b = int(len(args) / a) + 1
c = 1
plt.figure(figsize=(a * 3, b))
for i in args:
plt.subplot(b, a, c)
plt.axis('off')
plt.text(0, 0.04, i, color='k', fontsize=11)
plt.hlines(0, 0, 10, color=i, linestyles='solid', linewidth=25)
c = c + 1
plt.tight_layout()
plt.show()
return
print('\tfuntion plotColor created ...') | code |
73073936/cell_14 | [
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm, skew
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import warnings
import os
import pandas as pd
import numpy as np
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import math
import matplotlib.pyplot as plt
from scipy.stats import skew
import warnings
def ignore_warn(*args, **kwargs):
pass
warnings.warn = ignore_warn
from scipy import stats
from scipy.stats import norm, skew
training_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
testing_dataframe = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
fig, ax = plt.subplots()
ax.scatter(x = training_dataframe['GrLivArea'], y = training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()
#Deleting outliers
training_dataframe= training_dataframe.drop(training_dataframe[(training_dataframe['GrLivArea']>4000) & (training_dataframe['SalePrice']<300000)].index)
#Check the scatterplot again
fig, ax = plt.subplots()
ax.scatter(training_dataframe['GrLivArea'], training_dataframe['SalePrice'])
plt.ylabel('SalePrice', fontsize=13)
plt.xlabel('GrLivArea', fontsize=13)
plt.show()
#Plotting histogram
sns.distplot(training_dataframe['SalePrice'] , fit=norm);
# Fitted Parameters used by function
(mu, sigma) = norm.fit(training_dataframe['SalePrice'])
print( '\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma))
#Plotting the distribution
plt.legend(['Normal dist. ($\mu=$ {:.2f} and $\sigma=$ {:.2f} )'.format(mu, sigma)],
loc='best')
plt.ylabel('Frequency')
plt.title('SalePrice distribution')
#Plotting QQ-plot
fig = plt.figure()
res = stats.probplot(training_dataframe['SalePrice'], plot=plt)
plt.show()
training_dataframe['SalePrice'] = np.log1p(training_dataframe['SalePrice'])
sns.distplot(training_dataframe['SalePrice'], fit=norm)
mu, sigma = norm.fit(training_dataframe['SalePrice'])
print('\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma))
plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)], loc='best')
plt.ylabel('Frequency')
plt.title('SalePrice distribution')
fig = plt.figure()
res = stats.probplot(training_dataframe['SalePrice'], plot=plt)
plt.show() | code |
90146658/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
print(iris.DESCR) | code |
90146658/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys()) | code |
90146658/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
X = iris.data[:, 2:]
y = iris.target
iris = sns.load_dataset('iris')
sns.set_style('whitegrid')
sns.FacetGrid(iris, hue='species', height=6).map(plt.scatter, 'petal_width', 'petal_length').add_legend()
iris.head() | code |
90146658/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
X = iris.data[:, 2:]
y = iris.target
tree_clf = DecisionTreeClassifier(max_depth=2)
tree_clf.fit(X, y) | code |
90146658/cell_17 | [
"text_html_output_1.png"
] | from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
X = iris.data[:, 2:]
y = iris.target
iris = sns.load_dataset('iris')
sns.set_style('whitegrid')
sns.FacetGrid(iris, hue='species', height=6).map(plt.scatter, 'petal_width', 'petal_length').add_legend()
tree_clf = DecisionTreeClassifier(max_depth=2)
tree_clf.fit(X, y)
from sklearn.tree import export_graphviz
export_graphviz(tree_clf, out_file=image_path('iris_tree.dot'), feature_names=iris.feature_names[2:], class_names=iris.target_names, rounded=True, filled=True) | code |
90146658/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
X = iris.data[:, 2:]
y = iris.target
iris = sns.load_dataset('iris')
sns.set_style('whitegrid')
sns.FacetGrid(iris, hue='species', height=6).map(plt.scatter, 'petal_width', 'petal_length').add_legend() | code |
90146658/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn import datasets
iris = datasets.load_iris()
list(iris.keys())
X = iris.data[:, 2:]
y = iris.target
X[:5, :] | code |
32073950/cell_11 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
crime = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-present.csv')
arrest = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/arrest-data-from-2010-to-present.csv')
ocrd = [list(), list(), list()]
rprt = [list(), list(), list()]
datas = [list(crime['Date Occurred']), list(crime['Date Reported'])]
lists = [ocrd, rprt]
x = 0
while x < 2:
for i in datas[x]:
temp = i.split('-')
if len(str(temp[0])) == 7:
lists[x][0].append(str(temp[0])[3:8])
else:
lists[x][0].append(temp[0])
lists[x][1].append(str(temp[1]))
lists[x][2].append(str(temp[2])[0:2])
x += 1
dist = crime['Area Name'].unique()
rate = []
for i in dist:
x = len(crime.loc[crime['Area Name'] == i])
rate.append(x)
rate.sort(reverse=True)
crime19 = crime.loc[crime.RepY == '2019']
xxx = crime19.loc[(crime19.RepD == '01') & (crime19.RepM == '01')]
len(xxx.RepY) | code |
32073950/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32073950/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
crime = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-present.csv')
arrest = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/arrest-data-from-2010-to-present.csv')
ocrd = [list(), list(), list()]
rprt = [list(), list(), list()]
datas = [list(crime['Date Occurred']), list(crime['Date Reported'])]
lists = [ocrd, rprt]
x = 0
while x < 2:
for i in datas[x]:
temp = i.split('-')
if len(str(temp[0])) == 7:
lists[x][0].append(str(temp[0])[3:8])
else:
lists[x][0].append(temp[0])
lists[x][1].append(str(temp[1]))
lists[x][2].append(str(temp[2])[0:2])
x += 1
dist = crime['Area Name'].unique()
rate = []
for i in dist:
x = len(crime.loc[crime['Area Name'] == i])
rate.append(x)
rate.sort(reverse=True)
f, ax = plt.subplots(figsize=(15, 8))
sns.barplot(x=dist, y=rate)
plt.xticks(rotation=45)
plt.xlabel = 'Area'
plt.ylabel = 'Count'
plt.show() | code |
32073950/cell_15 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
crime = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-present.csv')
arrest = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/arrest-data-from-2010-to-present.csv')
ocrd = [list(), list(), list()]
rprt = [list(), list(), list()]
datas = [list(crime['Date Occurred']), list(crime['Date Reported'])]
lists = [ocrd, rprt]
x = 0
while x < 2:
for i in datas[x]:
temp = i.split('-')
if len(str(temp[0])) == 7:
lists[x][0].append(str(temp[0])[3:8])
else:
lists[x][0].append(temp[0])
lists[x][1].append(str(temp[1]))
lists[x][2].append(str(temp[2])[0:2])
x += 1
dist = crime['Area Name'].unique()
rate = []
for i in dist:
x = len(crime.loc[crime['Area Name'] == i])
rate.append(x)
rate.sort(reverse=True)
crime19 = crime.loc[crime.RepY == '2019']
xxx = crime19.loc[(crime19.RepD == '01') & (crime19.RepM == '01')]
len(xxx.RepY)
crimex = crime
crimesWo19 = crimex.loc[crimex.RepY != '2019']
Delay = []
Mouth = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
OD = list(crimesWo19.OccD)
RD = list(crimesWo19.RepD)
OM = list(crimesWo19.OccM)
for i in range(1888002):
od = int(OD[i])
rd = int(RD[i])
Day = rd - od
if Day < 0:
M = int(OM[i]) - 1
Ekstra = Mouth[M] - od
day = Ekstra + rd
Delay.append(day)
else:
Delay.append(Day)
crimesWo19['delay'] = Delay
crimesWo19.tail() | code |
32073950/cell_12 | [
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
crime = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-present.csv')
arrest = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/arrest-data-from-2010-to-present.csv')
ocrd = [list(), list(), list()]
rprt = [list(), list(), list()]
datas = [list(crime['Date Occurred']), list(crime['Date Reported'])]
lists = [ocrd, rprt]
x = 0
while x < 2:
for i in datas[x]:
temp = i.split('-')
if len(str(temp[0])) == 7:
lists[x][0].append(str(temp[0])[3:8])
else:
lists[x][0].append(temp[0])
lists[x][1].append(str(temp[1]))
lists[x][2].append(str(temp[2])[0:2])
x += 1
dist = crime['Area Name'].unique()
rate = []
for i in dist:
x = len(crime.loc[crime['Area Name'] == i])
rate.append(x)
rate.sort(reverse=True)
crime19 = crime.loc[crime.RepY == '2019']
xxx = crime19.loc[(crime19.RepD == '01') & (crime19.RepM == '01')]
len(xxx.RepY)
len(crime19.RepY) - len(xxx.RepY) | code |
32073950/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
import os
crime = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/crime-data-from-2010-to-present.csv')
arrest = pd.read_csv('/kaggle/input/los-angeles-crime-arrest-data/arrest-data-from-2010-to-present.csv')
crime.head() | code |
73079159/cell_13 | [
"text_html_output_1.png"
] | from datetime import datetime
from gluonts.dataset.common import ListDataset
from gluonts.dataset.field_names import FieldName
from gluonts.model.deepar import DeepAREstimator
from gluonts.mx.trainer import Trainer
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
### How the time series is for each Car model
### Some show upward trend some show downward trend
fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharex=True)
axx = axs.ravel()
for i in range(0, 4):
df[df.columns[i]].plot(ax=axx[i])
axx[i].set_xlabel("date")
axx[i].set_ylabel(f"{df.columns[i]} Car Sales")
axx[i].grid(which='minor', axis='x')
df_input = df.reset_index(drop=True).T.reset_index()
ts_code = df_input['index'].astype('category').cat.codes.values
df_train = df_input.iloc[:, 1:116].values
df_test = df_input.iloc[:, 116:].values
df_test.shape
df_train.shape
freq = 'M'
start_train = pd.Timestamp('2007-01-01', freq=freq)
start_test = pd.Timestamp('2016-07-01', freq=freq)
prediction_length = 2
estimator = DeepAREstimator(freq=freq, context_length=12, prediction_length=prediction_length, use_feat_static_cat=True, cardinality=[1], num_layers=2, num_cells=8, cell_type='lstm', trainer=Trainer(epochs=300, learning_rate=0.01, learning_rate_decay_factor=0.1))
from gluonts.dataset.common import ListDataset
from gluonts.dataset.field_names import FieldName
train_ds = ListDataset([{FieldName.TARGET: target, FieldName.START: start_train, FieldName.FEAT_STATIC_CAT: fsc} for target, fsc in zip(df_train, ts_code.reshape(-1, 1))], freq=freq)
test_ds = ListDataset([{FieldName.TARGET: target, FieldName.START: start_test, FieldName.FEAT_STATIC_CAT: fsc} for target, fsc in zip(df_test, ts_code.reshape(-1, 1))], freq=freq)
predictor = estimator.train(training_data=train_ds) | code |
73079159/cell_9 | [
"text_html_output_1.png"
] | from datetime import datetime
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
### How the time series is for each Car model
### Some show upward trend some show downward trend
fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharex=True)
axx = axs.ravel()
for i in range(0, 4):
df[df.columns[i]].plot(ax=axx[i])
axx[i].set_xlabel("date")
axx[i].set_ylabel(f"{df.columns[i]} Car Sales")
axx[i].grid(which='minor', axis='x')
df_input = df.reset_index(drop=True).T.reset_index()
ts_code = df_input['index'].astype('category').cat.codes.values
df_train = df_input.iloc[:, 1:116].values
df_test = df_input.iloc[:, 116:].values
df_train.shape | code |
73079159/cell_6 | [
"image_output_1.png"
] | from datetime import datetime
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharex=True)
axx = axs.ravel()
for i in range(0, 4):
df[df.columns[i]].plot(ax=axx[i])
axx[i].set_xlabel('date')
axx[i].set_ylabel(f'{df.columns[i]} Car Sales')
axx[i].grid(which='minor', axis='x') | code |
73079159/cell_29 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from datetime import datetime
from gluonts.dataset.common import ListDataset
from gluonts.dataset.common import ListDataset
from gluonts.dataset.field_names import FieldName
from gluonts.model.deepar import DeepAREstimator
from gluonts.model.deepar import DeepAREstimator
from gluonts.mx import Trainer
from gluonts.mx.trainer import Trainer
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
### How the time series is for each Car model
### Some show upward trend some show downward trend
fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharex=True)
axx = axs.ravel()
for i in range(0, 4):
df[df.columns[i]].plot(ax=axx[i])
axx[i].set_xlabel("date")
axx[i].set_ylabel(f"{df.columns[i]} Car Sales")
axx[i].grid(which='minor', axis='x')
df_input = df.reset_index(drop=True).T.reset_index()
ts_code = df_input['index'].astype('category').cat.codes.values
df_train = df_input.iloc[:, 1:116].values
df_test = df_input.iloc[:, 116:].values
df_test.shape
df_train.shape
freq = 'M'
start_train = pd.Timestamp('2007-01-01', freq=freq)
start_test = pd.Timestamp('2016-07-01', freq=freq)
prediction_length = 2
estimator = DeepAREstimator(freq=freq, context_length=12, prediction_length=prediction_length, use_feat_static_cat=True, cardinality=[1], num_layers=2, num_cells=8, cell_type='lstm', trainer=Trainer(epochs=300, learning_rate=0.01, learning_rate_decay_factor=0.1))
from gluonts.dataset.common import ListDataset
from gluonts.dataset.field_names import FieldName
train_ds = ListDataset([{FieldName.TARGET: target, FieldName.START: start_train, FieldName.FEAT_STATIC_CAT: fsc} for target, fsc in zip(df_train, ts_code.reshape(-1, 1))], freq=freq)
test_ds = ListDataset([{FieldName.TARGET: target, FieldName.START: start_test, FieldName.FEAT_STATIC_CAT: fsc} for target, fsc in zip(df_test, ts_code.reshape(-1, 1))], freq=freq)
predictor = estimator.train(training_data=train_ds)
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
make = make[make['Make'] == 'Ford']
df_input = make[['Year_Month', 'Quantity']]
df_input = df_input.set_index('Year_Month')
train_time = '2016-08-01'
prediction_length = 6
estimator = DeepAREstimator(freq='1M', context_length=12, prediction_length=prediction_length, num_layers=2, num_cells=128, cell_type='lstm', trainer=Trainer(epochs=20))
from gluonts.dataset.common import ListDataset
training_data = ListDataset([{'start': df_input.index[0], 'target': df_input.Quantity[:train_time]}], freq='1M')
predictor = estimator.train(training_data=training_data) | code |
73079159/cell_19 | [
"text_plain_output_1.png"
] | item_metrics | code |
73079159/cell_1 | [
"text_plain_output_1.png"
] | ## Install the package
#!pip install --upgrade mxnet-cu101==1.6.0.post0
!pip install --upgrade mxnet==1.6.0
!pip install gluonts | code |
73079159/cell_18 | [
"text_plain_output_1.png"
] | from datetime import datetime
from gluonts.evaluation import Evaluator
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
### How the time series is for each Car model
### Some show upward trend some show downward trend
fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharex=True)
axx = axs.ravel()
for i in range(0, 4):
df[df.columns[i]].plot(ax=axx[i])
axx[i].set_xlabel("date")
axx[i].set_ylabel(f"{df.columns[i]} Car Sales")
axx[i].grid(which='minor', axis='x')
df_input = df.reset_index(drop=True).T.reset_index()
ts_code = df_input['index'].astype('category').cat.codes.values
df_train = df_input.iloc[:, 1:116].values
df_test = df_input.iloc[:, 116:].values
df_test.shape
from tqdm.autonotebook import tqdm
tss = list(tqdm(ts_it, total=len(df_test)))
forecasts = list(tqdm(forecast_it, total=len(df_test)))
from gluonts.evaluation import Evaluator
evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9])
agg_metrics, item_metrics = evaluator(iter(tss), iter(forecasts), num_series=len(df_test)) | code |
73079159/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
### How the time series is for each Car model
### Some show upward trend some show downward trend
fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharex=True)
axx = axs.ravel()
for i in range(0, 4):
df[df.columns[i]].plot(ax=axx[i])
axx[i].set_xlabel("date")
axx[i].set_ylabel(f"{df.columns[i]} Car Sales")
axx[i].grid(which='minor', axis='x')
df_input = df.reset_index(drop=True).T.reset_index()
ts_code = df_input['index'].astype('category').cat.codes.values
df_train = df_input.iloc[:, 1:116].values
df_test = df_input.iloc[:, 116:].values
df_test.shape | code |
73079159/cell_15 | [
"text_html_output_1.png"
] | from datetime import datetime
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
### How the time series is for each Car model
### Some show upward trend some show downward trend
fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharex=True)
axx = axs.ravel()
for i in range(0, 4):
df[df.columns[i]].plot(ax=axx[i])
axx[i].set_xlabel("date")
axx[i].set_ylabel(f"{df.columns[i]} Car Sales")
axx[i].grid(which='minor', axis='x')
df_input = df.reset_index(drop=True).T.reset_index()
ts_code = df_input['index'].astype('category').cat.codes.values
df_train = df_input.iloc[:, 1:116].values
df_test = df_input.iloc[:, 116:].values
df_test.shape
from tqdm.autonotebook import tqdm
print('Obtaining time series conditioning values ...')
tss = list(tqdm(ts_it, total=len(df_test)))
print('Obtaining time series predictions ...')
forecasts = list(tqdm(forecast_it, total=len(df_test))) | code |
73079159/cell_3 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from datetime import datetime
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
make.head(3) | code |
73079159/cell_17 | [
"image_output_1.png"
] | from datetime import datetime
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
### How the time series is for each Car model
### Some show upward trend some show downward trend
fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharex=True)
axx = axs.ravel()
for i in range(0, 4):
df[df.columns[i]].plot(ax=axx[i])
axx[i].set_xlabel("date")
axx[i].set_ylabel(f"{df.columns[i]} Car Sales")
axx[i].grid(which='minor', axis='x')
df_input = df.reset_index(drop=True).T.reset_index()
ts_code = df_input['index'].astype('category').cat.codes.values
df_train = df_input.iloc[:, 1:116].values
df_test = df_input.iloc[:, 116:].values
df_test.shape
freq = 'M'
start_train = pd.Timestamp('2007-01-01', freq=freq)
start_test = pd.Timestamp('2016-07-01', freq=freq)
prediction_length = 2
from tqdm.autonotebook import tqdm
tss = list(tqdm(ts_it, total=len(df_test)))
forecasts = list(tqdm(forecast_it, total=len(df_test)))
def plot_prob_forecasts(ts_entry, forecast_entry):
plot_length = prediction_length
prediction_intervals = (80.0, 95.0)
legend = ["observations", "median prediction"] + [f"{k}% prediction interval" for k in prediction_intervals][::-1]
fig, ax = plt.subplots(1, 1, figsize=(10, 7))
ts_entry[-plot_length:].plot(ax=ax)
forecast_entry.plot(prediction_intervals=prediction_intervals, color='g')
plt.grid(which="both")
plt.legend(legend, loc="upper left")
plt.show()
for i in tqdm(range(5)):
ts_entry = tss[i]
forecast_entry = forecasts[i]
plot_prob_forecasts(ts_entry, forecast_entry) | code |
73079159/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
### How the time series is for each Car model
### Some show upward trend some show downward trend
fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharex=True)
axx = axs.ravel()
for i in range(0, 4):
df[df.columns[i]].plot(ax=axx[i])
axx[i].set_xlabel("date")
axx[i].set_ylabel(f"{df.columns[i]} Car Sales")
axx[i].grid(which='minor', axis='x')
df_input = df.reset_index(drop=True).T.reset_index()
ts_code = df_input['index'].astype('category').cat.codes.values
df_train = df_input.iloc[:, 1:116].values
df_test = df_input.iloc[:, 116:].values
df_test.shape
freq = 'M'
start_train = pd.Timestamp('2007-01-01', freq=freq)
start_test = pd.Timestamp('2016-07-01', freq=freq)
prediction_length = 2
from tqdm.autonotebook import tqdm
tss = list(tqdm(ts_it, total=len(df_test)))
forecasts = list(tqdm(forecast_it, total=len(df_test)))
def plot_prob_forecasts(ts_entry, forecast_entry):
plot_length = prediction_length
prediction_intervals = (80.0, 95.0)
legend = ["observations", "median prediction"] + [f"{k}% prediction interval" for k in prediction_intervals][::-1]
fig, ax = plt.subplots(1, 1, figsize=(10, 7))
ts_entry[-plot_length:].plot(ax=ax)
forecast_entry.plot(prediction_intervals=prediction_intervals, color='g')
plt.grid(which="both")
plt.legend(legend, loc="upper left")
plt.show()
for i in tqdm(range(5)):
ts_entry = tss[i]
forecast_entry = forecasts[i]
train_time = '2016-08-01'
prediction_length = 6
forecasts = list(forecast_it)
tss = list(ts_it)
forecast_entry = forecasts[0]
def plot_prob_forecasts(ts_entry, forecast_entry):
plot_length = prediction_length
prediction_intervals = (80.0, 95.0)
legend = ["observations", "median prediction"] + [f"{k}% prediction interval" for k in prediction_intervals][::-1]
fig, ax = plt.subplots(1, 1, figsize=(10, 7))
ts_entry[-plot_length:].plot(ax=ax)
forecast_entry.plot(prediction_intervals=prediction_intervals, color='g')
plt.grid(which="both")
plt.legend(legend, loc="upper left")
plt.show()
plot_prob_forecasts(tss[0], forecasts[0]) | code |
73079159/cell_22 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
import pandas as pd
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
freq = 'M'
start_train = pd.Timestamp('2007-01-01', freq=freq)
start_test = pd.Timestamp('2016-07-01', freq=freq)
prediction_length = 2
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
make.head(3) | code |
73079159/cell_37 | [
"text_html_output_1.png"
] | item_metrics | code |
73079159/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
df.head() | code |
73079159/cell_36 | [
"text_html_output_1.png"
] | from datetime import datetime
from gluonts.dataset.common import ListDataset
from gluonts.dataset.common import ListDataset
from gluonts.evaluation import Evaluator
from gluonts.evaluation import Evaluator
from tqdm.autonotebook import tqdm
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
def subset_data(dataframe=make, subset=['Toyota', 'Ford', 'BMW', 'Honda', 'Peugeot']):
"""
Function to subset the data
INPUT : dataframe to subset and car type list
OUTPUT : 5 different dataframes with the subset data
"""
dataframe.set_index('Year_Month', inplace=True)
toyota = dataframe[dataframe['Make'] == subset[0]]
ford = dataframe[dataframe['Make'] == subset[1]]
honda = dataframe[dataframe['Make'] == subset[3]]
BMW = dataframe[dataframe['Make'] == subset[2]]
peugeot = dataframe[dataframe['Make'] == subset[4]]
df = pd.concat([toyota['Quantity'], ford['Quantity'], BMW['Quantity'], honda['Quantity'], peugeot['Quantity']], axis=1)
df.columns = [subset[0], subset[1], subset[2], subset[3], subset[4]]
return df
df = subset_data()
### How the time series is for each Car model
### Some show upward trend some show downward trend
fig, axs = plt.subplots(2, 2, figsize=(20, 20), sharex=True)
axx = axs.ravel()
for i in range(0, 4):
df[df.columns[i]].plot(ax=axx[i])
axx[i].set_xlabel("date")
axx[i].set_ylabel(f"{df.columns[i]} Car Sales")
axx[i].grid(which='minor', axis='x')
df_input = df.reset_index(drop=True).T.reset_index()
ts_code = df_input['index'].astype('category').cat.codes.values
df_train = df_input.iloc[:, 1:116].values
df_test = df_input.iloc[:, 116:].values
df_test.shape
freq = 'M'
start_train = pd.Timestamp('2007-01-01', freq=freq)
start_test = pd.Timestamp('2016-07-01', freq=freq)
prediction_length = 2
from tqdm.autonotebook import tqdm
tss = list(tqdm(ts_it, total=len(df_test)))
forecasts = list(tqdm(forecast_it, total=len(df_test)))
from gluonts.evaluation import Evaluator
evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9])
agg_metrics, item_metrics = evaluator(iter(tss), iter(forecasts), num_series=len(df_test))
def convert_to_date(x):
return datetime.strptime(x, '%Y %m')
make = pd.read_csv('../input/newcarsalesnorway/norway_new_car_sales_by_make.csv', parse_dates=[['Year', 'Month']], date_parser=convert_to_date)
make = make[['Year_Month', 'Quantity', 'Make']]
make = make[make['Make'] == 'Ford']
df_input = make[['Year_Month', 'Quantity']]
df_input = df_input.set_index('Year_Month')
train_time = '2016-08-01'
prediction_length = 6
from gluonts.dataset.common import ListDataset
training_data = ListDataset([{'start': df_input.index[0], 'target': df_input.Quantity[:train_time]}], freq='1M')
test_data = ListDataset([{'start': df_input.index[0], 'target': df_input.Quantity[:'2017-01-01']}], freq='1M')
forecasts = list(forecast_it)
tss = list(ts_it)
from gluonts.evaluation import Evaluator
evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9])
agg_metrics, item_metrics = evaluator(iter(tss), iter(forecasts), num_series=len(test_data)) | code |
2014978/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | df.Department.groupby(df.Department).count().plot(kind='bar')
Specialization = 'Area of Specialization/Research Interests'
df[Specialization].value_counts().sort_values()[::-1][:20].plot(kind='bar') | code |
2014978/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | df.Department.groupby(df.Department).count().plot(kind='bar') | code |
2014978/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import plotly.plotly as py
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
plt.rcParams['figure.figsize'] = (12, 5)
df = pd.read_csv('../input/Pakistan Intellectual Capital - Computer Science - Ver 1.csv', encoding='ISO-8859-1')
df.head() | code |
2014978/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | df.Department.groupby(df.Department).count().plot(kind='bar')
province = 'Province University Located'
df[province].value_counts().sort_values().plot(kind='bar') | code |
2014978/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | df.Department.groupby(df.Department).count().plot(kind='bar')
df['Country'].value_counts().sort_values()[::-1][1:].plot(kind='bar') | code |
17132381/cell_42 | [
"text_plain_output_1.png"
] | acts = hook_a.stored[0].cpu()
acts.shape | code |
17132381/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
bs = 64
sz = 224
tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=360, max_warp=0, max_zoom=1.1, max_lighting=0.1, p_lighting=0.5)
src = ImageList.from_df(df=df, path='./', cols='path').split_by_rand_pct(0.2).label_from_df(cols='diagnosis')
data = src.transform(tfms, size=sz, resize_method=ResizeMethod.SQUISH, padding_mode='zeros').databunch(bs=bs, num_workers=4).normalize(imagenet_stats)
from sklearn.metrics import cohen_kappa_score
def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(y_hat.argmax(dim=-1), y, weights='quadratic'), device='cuda:0')
learn = cnn_learner(data, base_arch=models.resnet50, metrics=[quadratic_kappa])
learn.fit_one_cycle(4, max_lr=0.01) | code |
17132381/cell_13 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
df['diagnosis'].hist(figsize=(10, 5)) | code |
17132381/cell_23 | [
"text_html_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
bs = 64
sz = 224
tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=360, max_warp=0, max_zoom=1.1, max_lighting=0.1, p_lighting=0.5)
src = ImageList.from_df(df=df, path='./', cols='path').split_by_rand_pct(0.2).label_from_df(cols='diagnosis')
data = src.transform(tfms, size=sz, resize_method=ResizeMethod.SQUISH, padding_mode='zeros').databunch(bs=bs, num_workers=4).normalize(imagenet_stats)
from sklearn.metrics import cohen_kappa_score
def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(y_hat.argmax(dim=-1), y, weights='quadratic'), device='cuda:0')
learn = cnn_learner(data, base_arch=models.resnet50, metrics=[quadratic_kappa])
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.fit_one_cycle(6, max_lr=slice(1e-06, 0.001)) | code |
17132381/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
os.listdir('../input') | code |
17132381/cell_26 | [
"text_html_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
bs = 64
sz = 224
tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=360, max_warp=0, max_zoom=1.1, max_lighting=0.1, p_lighting=0.5)
src = ImageList.from_df(df=df, path='./', cols='path').split_by_rand_pct(0.2).label_from_df(cols='diagnosis')
data = src.transform(tfms, size=sz, resize_method=ResizeMethod.SQUISH, padding_mode='zeros').databunch(bs=bs, num_workers=4).normalize(imagenet_stats)
from sklearn.metrics import cohen_kappa_score
def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(y_hat.argmax(dim=-1), y, weights='quadratic'), device='cuda:0')
learn = cnn_learner(data, base_arch=models.resnet50, metrics=[quadratic_kappa])
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.fit_one_cycle(6, max_lr=slice(1e-06, 0.001))
interp = ClassificationInterpretation.from_learner(learn)
losses, idxs = interp.top_losses()
len(data.valid_ds) == len(losses) == len(idxs) | code |
17132381/cell_11 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
len_df = len(df)
print(f'There are {len_df} images') | code |
17132381/cell_7 | [
"image_output_1.png"
] | print('Make sure cudnn is enabled:', torch.backends.cudnn.enabled) | code |
17132381/cell_49 | [
"text_plain_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import matplotlib.pyplot as plt
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
bs = 64
sz = 224
tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=360, max_warp=0, max_zoom=1.1, max_lighting=0.1, p_lighting=0.5)
src = ImageList.from_df(df=df, path='./', cols='path').split_by_rand_pct(0.2).label_from_df(cols='diagnosis')
data = src.transform(tfms, size=sz, resize_method=ResizeMethod.SQUISH, padding_mode='zeros').databunch(bs=bs, num_workers=4).normalize(imagenet_stats)
from sklearn.metrics import cohen_kappa_score
def quadratic_kappa(y_hat, y):
return torch.tensor(cohen_kappa_score(y_hat.argmax(dim=-1), y, weights='quadratic'), device='cuda:0')
learn = cnn_learner(data, base_arch=models.resnet50, metrics=[quadratic_kappa])
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.fit_one_cycle(6, max_lr=slice(1e-06, 0.001))
interp = ClassificationInterpretation.from_learner(learn)
losses, idxs = interp.top_losses()
len(data.valid_ds) == len(losses) == len(idxs)
idx = 1
im, cl = learn.data.dl(DatasetType.Valid).dataset[idx]
cl = int(cl)
xb, _ = data.one_item(im)
xb_im = Image(data.denorm(xb)[0])
xb = xb.cuda()
m = learn.model.eval()
acts = hook_a.stored[0].cpu()
acts.shape
grad = hook_g.stored[0][0].cpu()
grad.shape
grad_chan = grad.mean(1).mean(1)
grad_chan.shape
mult = F.relu((acts * grad_chan[..., None, None]).sum(0))
mult.shape
#Utility function to display heatmap:
def show_heatmap(hm):
_,ax = plt.subplots()
sz = list(xb_im.shape[-2:])
xb_im.show(ax,title=f"pred. class: {interp.pred_class[idx]}, actual class: {learn.data.classes[cl]}")
ax.imshow(hm, alpha=0.6, extent=(0,*sz[::-1],0),
interpolation='bilinear', cmap='magma')
return _,ax
show_heatmap(mult) | code |
17132381/cell_16 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(SEED)
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir, 'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir, '{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True)
bs = 64
sz = 224
tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=360, max_warp=0, max_zoom=1.1, max_lighting=0.1, p_lighting=0.5)
src = ImageList.from_df(df=df, path='./', cols='path').split_by_rand_pct(0.2).label_from_df(cols='diagnosis')
data = src.transform(tfms, size=sz, resize_method=ResizeMethod.SQUISH, padding_mode='zeros').databunch(bs=bs, num_workers=4).normalize(imagenet_stats)
data.show_batch(rows=3, figsize=(7, 6)) | code |
17132381/cell_47 | [
"text_plain_output_1.png"
] | acts = hook_a.stored[0].cpu()
acts.shape
grad = hook_g.stored[0][0].cpu()
grad.shape
grad_chan = grad.mean(1).mean(1)
grad_chan.shape
mult = F.relu((acts * grad_chan[..., None, None]).sum(0))
mult.shape | code |
17132381/cell_43 | [
"text_plain_output_1.png"
] | grad = hook_g.stored[0][0].cpu()
grad.shape | code |
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