path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
88100444/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
big_dance.head() | code |
88100444/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 |
88100444/cell_7 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()
y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', '3Pper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]
from sklearn.tree import DecisionTreeRegressor
bd_model = DecisionTreeRegressor(random_state=1)
bd_model.fit(X, y)
from sklearn.model_selection import train_test_split
train_X, val_X, train_y, val_y = train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
bd_model.fit(train_X, train_y)
from sklearn.metrics import mean_absolute_error
val_predictions = bd_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
print(val_mae) | code |
88100444/cell_8 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()
y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', '3Pper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]
from sklearn.tree import DecisionTreeRegressor
bd_model = DecisionTreeRegressor(random_state=1)
bd_model.fit(X, y)
from sklearn.model_selection import train_test_split
train_X, val_X, train_y, val_y = train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
bd_model.fit(train_X, train_y)
from sklearn.metrics import mean_absolute_error
val_predictions = bd_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
set_up = [[25.7, 58.3, 33.9, 12.5, 17.5, 16.7, 71.4]]
bd_model.predict(set_up) | code |
88100444/cell_3 | [
"image_output_1.png"
] | from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
figure(figsize=(8, 6), dpi=80)
corrMatrix = big_dance.corr()
sn.heatmap(corrMatrix, annot=False)
plt.show() | code |
88100444/cell_5 | [
"text_plain_output_1.png"
] | from matplotlib.pyplot import figure
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
big_dance = pd.read_csv('../input/mm-data-prediction/MM_score_predictionv2.csv')
import seaborn as sn
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
corrMatrix = big_dance.corr()
y = big_dance['Total_Score_March_Madness']
features = ['FG', 'FGA', '3Pper', 'FT', 'FTA', 'PF', 'PTS']
X = big_dance[features]
from sklearn.tree import DecisionTreeRegressor
bd_model = DecisionTreeRegressor(random_state=1)
bd_model.fit(X, y) | code |
1005328/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
import pandas as pd
import pandas as pd
with open('../input/train.json') as train_json:
raw_train = pd.read_json(train_json.read()).reset_index()
from sklearn.neighbors import KNeighborsRegressor
model = KNeighborsRegressor(n_neighbors=300)
price_df = pd.concat([raw_train['bedrooms'], raw_train['bathrooms'], raw_train['latitude'], raw_train['longitude'], raw_train['price']], axis=1)
model.fit(price_df.drop(['price'], axis=1), price_df['price'])
print(model.kneighbors(price_df.drop(['price'], axis=1).loc[2].reshape(1, -1), n_neighbors=300)) | code |
1005328/cell_2 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
import pandas as pd
import pandas as pd
with open('../input/train.json') as train_json:
raw_train = pd.read_json(train_json.read()).reset_index()
from sklearn.neighbors import KNeighborsRegressor
model = KNeighborsRegressor(n_neighbors=300)
price_df = pd.concat([raw_train['bedrooms'], raw_train['bathrooms'], raw_train['latitude'], raw_train['longitude'], raw_train['price']], axis=1)
model.fit(price_df.drop(['price'], axis=1), price_df['price']) | code |
1005328/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
new_price_df = price_df[price_df['pred_price_ratio'] < 4]
plt.figure(figsize=(10, 20))
sns.boxplot(x='interest_level', y='pred_price_ratio', data=new_price_df)
plt.title('Interest Level and Price / Predicted Price Ratio', fontsize=32)
plt.show() | code |
1005328/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
import pandas as pd
import pandas as pd
with open('../input/train.json') as train_json:
raw_train = pd.read_json(train_json.read()).reset_index()
from sklearn.neighbors import KNeighborsRegressor
model = KNeighborsRegressor(n_neighbors=300)
price_df = pd.concat([raw_train['bedrooms'], raw_train['bathrooms'], raw_train['latitude'], raw_train['longitude'], raw_train['price']], axis=1)
model.fit(price_df.drop(['price'], axis=1), price_df['price'])
print(price_df.drop(['price'], axis=1).loc[2])
print(price_df.drop(['price'], axis=1).loc[311]) | code |
33119806/cell_21 | [
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape
df.drop_duplicates(subset=['statement'], keep='first', inplace=True)
text = ' '.join([x for x in df.statement])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'neutral']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'positive']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'negative']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
sns.countplot(df.sentiment) | code |
33119806/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape
df.drop_duplicates(subset=['statement'], keep='first', inplace=True)
df.info() | code |
33119806/cell_34 | [
"text_plain_output_1.png"
] | model_outputs | code |
33119806/cell_30 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-base-cased', num_labels=3, args={'reprocess_input_data': True, 'overwrite_output_dir': True}, use_cuda=False)
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
train_df = pd.DataFrame({'text': train['statement'][:1500].replace('\\n', ' ', regex=True), 'label': train['label'][:1500]})
eval_df = pd.DataFrame({'text': eva['statement'][-400:].replace('\\n', ' ', regex=True), 'label': eva['label'][-400:]})
model.train_model(train_df) | code |
33119806/cell_33 | [
"text_plain_output_4.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | result | code |
33119806/cell_44 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import numpy as np
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-base-cased', num_labels=3, args={'reprocess_input_data': True, 'overwrite_output_dir': True}, use_cuda=False)
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
train_df = pd.DataFrame({'text': train['statement'][:1500].replace('\\n', ' ', regex=True), 'label': train['label'][:1500]})
eval_df = pd.DataFrame({'text': eva['statement'][-400:].replace('\\n', ' ', regex=True), 'label': eva['label'][-400:]})
model.train_model(train_df)
result, model_outputs, wrong_predictions = model.eval_model(eval_df)
lst = []
for arr in model_outputs:
lst.append(np.argmax(arr))
def get_result(statement):
result = model.predict([statement])
pos = np.where(result[1][0] == np.amax(result[1][0]))
pos = int(pos[0])
sentiment_dict = {0: 'positive', 1: 'negative', 2: 'neutral'}
return
get_result("According to the company 's updated strategy for the years 2009-2012 , Basware targets a long-term net sales growth in the range of 20 % -40 % with an operating profit margin of 10 % -20 % of net sales .") | code |
33119806/cell_40 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import sklearn
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
train_df = pd.DataFrame({'text': train['statement'][:1500].replace('\\n', ' ', regex=True), 'label': train['label'][:1500]})
eval_df = pd.DataFrame({'text': eva['statement'][-400:].replace('\\n', ' ', regex=True), 'label': eva['label'][-400:]})
lst = []
for arr in model_outputs:
lst.append(np.argmax(arr))
true = eval_df['label'].tolist()
predicted = lst
import sklearn
mat = sklearn.metrics.confusion_matrix(true, predicted)
mat
sklearn.metrics.classification_report(true, predicted, target_names=['positive', 'neutral', 'negative'])
sklearn.metrics.accuracy_score(true, predicted) | code |
33119806/cell_39 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import sklearn
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
train_df = pd.DataFrame({'text': train['statement'][:1500].replace('\\n', ' ', regex=True), 'label': train['label'][:1500]})
eval_df = pd.DataFrame({'text': eva['statement'][-400:].replace('\\n', ' ', regex=True), 'label': eva['label'][-400:]})
lst = []
for arr in model_outputs:
lst.append(np.argmax(arr))
true = eval_df['label'].tolist()
predicted = lst
import sklearn
mat = sklearn.metrics.confusion_matrix(true, predicted)
mat
sklearn.metrics.classification_report(true, predicted, target_names=['positive', 'neutral', 'negative']) | code |
33119806/cell_26 | [
"text_plain_output_1.png"
] | !pip install simpletransformers | code |
33119806/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape
df.info() | code |
33119806/cell_19 | [
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape
df.drop_duplicates(subset=['statement'], keep='first', inplace=True)
text = ' '.join([x for x in df.statement])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'neutral']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'positive']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'negative']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.figure(figsize=(8, 6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show() | code |
33119806/cell_45 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import numpy as np
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-base-cased', num_labels=3, args={'reprocess_input_data': True, 'overwrite_output_dir': True}, use_cuda=False)
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
train_df = pd.DataFrame({'text': train['statement'][:1500].replace('\\n', ' ', regex=True), 'label': train['label'][:1500]})
eval_df = pd.DataFrame({'text': eva['statement'][-400:].replace('\\n', ' ', regex=True), 'label': eva['label'][-400:]})
model.train_model(train_df)
result, model_outputs, wrong_predictions = model.eval_model(eval_df)
lst = []
for arr in model_outputs:
lst.append(np.argmax(arr))
def get_result(statement):
result = model.predict([statement])
pos = np.where(result[1][0] == np.amax(result[1][0]))
pos = int(pos[0])
sentiment_dict = {0: 'positive', 1: 'negative', 2: 'neutral'}
return
get_result('Sales in Finland decreased by 2.0 % , and international sales decreased by 9.3 % in terms of euros , and by 15.1 % in terms of local currencies .') | code |
33119806/cell_18 | [
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape
df.drop_duplicates(subset=['statement'], keep='first', inplace=True)
text = ' '.join([x for x in df.statement])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'neutral']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'positive']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.figure(figsize=(8, 6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show() | code |
33119806/cell_28 | [
"text_plain_output_5.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
print(train.shape) | code |
33119806/cell_8 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df.head() | code |
33119806/cell_16 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape
df.drop_duplicates(subset=['statement'], keep='first', inplace=True)
text = ' '.join([x for x in df.statement])
wordcloud = WordCloud(background_color='white').generate(text)
plt.figure(figsize=(8, 6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show() | code |
33119806/cell_38 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape
df.drop_duplicates(subset=['statement'], keep='first', inplace=True)
text = ' '.join([x for x in df.statement])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'neutral']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'positive']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'negative']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
train_df = pd.DataFrame({'text': train['statement'][:1500].replace('\\n', ' ', regex=True), 'label': train['label'][:1500]})
eval_df = pd.DataFrame({'text': eva['statement'][-400:].replace('\\n', ' ', regex=True), 'label': eva['label'][-400:]})
lst = []
for arr in model_outputs:
lst.append(np.argmax(arr))
true = eval_df['label'].tolist()
predicted = lst
import sklearn
mat = sklearn.metrics.confusion_matrix(true, predicted)
mat
df_cm = pd.DataFrame(mat, range(3), range(3))
sns.heatmap(df_cm, annot=True)
plt.show() | code |
33119806/cell_17 | [
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape
df.drop_duplicates(subset=['statement'], keep='first', inplace=True)
text = ' '.join([x for x in df.statement])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'neutral']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.figure(figsize=(8, 6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show() | code |
33119806/cell_43 | [
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import numpy as np
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-base-cased', num_labels=3, args={'reprocess_input_data': True, 'overwrite_output_dir': True}, use_cuda=False)
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
train_df = pd.DataFrame({'text': train['statement'][:1500].replace('\\n', ' ', regex=True), 'label': train['label'][:1500]})
eval_df = pd.DataFrame({'text': eva['statement'][-400:].replace('\\n', ' ', regex=True), 'label': eva['label'][-400:]})
model.train_model(train_df)
result, model_outputs, wrong_predictions = model.eval_model(eval_df)
lst = []
for arr in model_outputs:
lst.append(np.argmax(arr))
def get_result(statement):
result = model.predict([statement])
pos = np.where(result[1][0] == np.amax(result[1][0]))
pos = int(pos[0])
sentiment_dict = {0: 'positive', 1: 'negative', 2: 'neutral'}
return
get_result('According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .') | code |
33119806/cell_31 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_7.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-base-cased', num_labels=3, args={'reprocess_input_data': True, 'overwrite_output_dir': True}, use_cuda=False)
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
train_df = pd.DataFrame({'text': train['statement'][:1500].replace('\\n', ' ', regex=True), 'label': train['label'][:1500]})
eval_df = pd.DataFrame({'text': eva['statement'][-400:].replace('\\n', ' ', regex=True), 'label': eva['label'][-400:]})
model.train_model(train_df)
result, model_outputs, wrong_predictions = model.eval_model(eval_df) | code |
33119806/cell_46 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
import numpy as np
import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-base-cased', num_labels=3, args={'reprocess_input_data': True, 'overwrite_output_dir': True}, use_cuda=False)
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
train_df = pd.DataFrame({'text': train['statement'][:1500].replace('\\n', ' ', regex=True), 'label': train['label'][:1500]})
eval_df = pd.DataFrame({'text': eva['statement'][-400:].replace('\\n', ' ', regex=True), 'label': eva['label'][-400:]})
model.train_model(train_df)
result, model_outputs, wrong_predictions = model.eval_model(eval_df)
lst = []
for arr in model_outputs:
lst.append(np.argmax(arr))
def get_result(statement):
result = model.predict([statement])
pos = np.where(result[1][0] == np.amax(result[1][0]))
pos = int(pos[0])
sentiment_dict = {0: 'positive', 1: 'negative', 2: 'neutral'}
return
statement = 'Give your statement'
get_result(statement) | code |
33119806/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape
df.drop_duplicates(subset=['statement'], keep='first', inplace=True)
df.describe() | code |
33119806/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape
df.drop_duplicates(subset=['statement'], keep='first', inplace=True)
text = ' '.join([x for x in df.statement])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'neutral']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'positive']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
text = ' '.join([x for x in df.statement[df.sentiment == 'negative']])
wordcloud = WordCloud(background_color='white').generate(text)
plt.axis('off')
df['sentiment'].value_counts() | code |
33119806/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape | code |
33119806/cell_27 | [
"text_plain_output_1.png"
] | from simpletransformers.classification import ClassificationModel
from simpletransformers.classification import ClassificationModel
model = ClassificationModel('bert', 'bert-base-cased', num_labels=3, args={'reprocess_input_data': True, 'overwrite_output_dir': True}, use_cuda=False) | code |
33119806/cell_37 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import sklearn
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
def making_label(st):
if st == 'positive':
return 0
elif st == 'neutral':
return 2
else:
return 1
train['label'] = train['sentiment'].apply(making_label)
eva['label'] = eva['sentiment'].apply(making_label)
train_df = pd.DataFrame({'text': train['statement'][:1500].replace('\\n', ' ', regex=True), 'label': train['label'][:1500]})
eval_df = pd.DataFrame({'text': eva['statement'][-400:].replace('\\n', ' ', regex=True), 'label': eva['label'][-400:]})
lst = []
for arr in model_outputs:
lst.append(np.argmax(arr))
true = eval_df['label'].tolist()
predicted = lst
import sklearn
mat = sklearn.metrics.confusion_matrix(true, predicted)
mat | code |
33119806/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sentiment-analysis-for-financial-news/all-data.csv', encoding='ISO-8859-1')
df = df.rename(columns={'neutral': 'sentiment', 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'statement'})
df.shape
df.describe() | code |
90144662/cell_13 | [
"text_plain_output_1.png"
] | from pdf2image import convert_from_path,convert_from_bytes
import easyocr
PDF_file = '../input/osbook/operating-system-concepts-9th-edition.pdf'
'\nPart #1 : Converting PDF to images\n'
pages = convert_from_path(PDF_file, dpi=100, thread_count=4)
type(pages[0])
image_counter = 1
for page in pages:
filename = 'page_' + str(image_counter) + '.jpg'
page.save(filename, 'JPEG')
image_counter = image_counter + 1
reader = easyocr.Reader(['en'])
filelimit = image_counter - 1
outfile = 'out_text.txt'
text = ''
f = open(outfile, 'a')
for i in range(1, image_counter - 1):
filename = 'page_' + str(i) + '.jpg'
result = reader.readtext(filename, paragraph='True')
for i in result:
text = text + '\n' + i[1]
PDF_file = '../input/osbook/2011_EST_OS.pdf'
image_counter = 1
pages = convert_from_path(PDF_file, dpi=150, thread_count=4, last_page=200)
fileAll = []
for page in pages:
filename = 'page_' + str(image_counter) + '.jpg'
page.save(filename, 'JPEG')
image_counter = image_counter + 1
fileAll.append(filename)
reader = easyocr.Reader(['en'])
a = reader.readtext_batched(fileAll[:100], paragraph='True')
for i in a:
print()
for j in i:
print(j) | code |
90144662/cell_4 | [
"text_plain_output_1.png"
] | !apt-get install poppler-utils -y | code |
90144662/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 |
90144662/cell_14 | [
"text_plain_output_1.png"
] | from pdf2image import convert_from_path,convert_from_bytes
import easyocr
PDF_file = '../input/osbook/operating-system-concepts-9th-edition.pdf'
'\nPart #1 : Converting PDF to images\n'
pages = convert_from_path(PDF_file, dpi=100, thread_count=4)
type(pages[0])
image_counter = 1
for page in pages:
filename = 'page_' + str(image_counter) + '.jpg'
page.save(filename, 'JPEG')
image_counter = image_counter + 1
reader = easyocr.Reader(['en'])
filelimit = image_counter - 1
outfile = 'out_text.txt'
text = ''
f = open(outfile, 'a')
for i in range(1, image_counter - 1):
filename = 'page_' + str(i) + '.jpg'
result = reader.readtext(filename, paragraph='True')
for i in result:
text = text + '\n' + i[1]
PDF_file = '../input/osbook/2011_EST_OS.pdf'
image_counter = 1
pages = convert_from_path(PDF_file, dpi=150, thread_count=4, last_page=200)
fileAll = []
for page in pages:
filename = 'page_' + str(image_counter) + '.jpg'
page.save(filename, 'JPEG')
image_counter = image_counter + 1
fileAll.append(filename)
reader = easyocr.Reader(['en'])
a = reader.readtext_batched(fileAll[:100], paragraph='True')
for i in a:
print()
for j in i:
print(j[1]) | code |
16164242/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import KFold
import pandas as pd
TEST_SIZE = 0.33
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
RANDOM_STATE = 123
N_SPLITS = 3
VERBOSE = False
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
def read_data(dataset='train', data_path=DATA_PATH):
"""
"""
index_col = None
index_type = ['train', 'test']
if dataset in index_type:
index_col = 'id'
data_path = csv_path(dataset, data_path=data_path)
return pd.read_csv(data_path, index_col=index_col)
train = read_data('train')
test = read_data('test')
molecule_train = pd.DataFrame({'molecule_name': train['molecule_name'].unique()})
molecule_test = pd.DataFrame({'molecule_name': test['molecule_name'].unique()})
structures = read_data('structures')
atom_list_df = structures.groupby('molecule_name')['atom'].apply(list)
atom_list_df = atom_list_df.to_frame()
if FREE_MEMORY:
del train
del test
molecule_train = pd.merge(molecule_train, atom_list_df, how='left', on='molecule_name')
molecule_test = pd.merge(molecule_test, atom_list_df, how='left', on='molecule_name')
potential_energy = read_data('potential_energy')
molecule_train = pd.merge(molecule_train, potential_energy)
if FREE_MEMORY:
del potential_energy
id_feature = 'molecule_name'
target_feature = (set(molecule_train) - set(molecule_test)).pop()
selected_features = list(molecule_test)
selected_features.remove(id_feature)
selected_features.remove('atom')
X = molecule_train[selected_features]
y = molecule_train[target_feature]
kfold = KFold(n_splits=N_SPLITS, random_state=RANDOM_STATE)
fold = 0
r2_scores = []
mse_scores = []
lin_reg = LinearRegression()
for in_index, oof_index in kfold.split(X, y):
fold += 1
print('- Training Fold: ({}/{})'.format(fold, N_SPLITS))
X_in, X_oof = (X.loc[in_index], X.loc[oof_index])
y_in, y_oof = (y.loc[in_index], y.loc[oof_index])
lin_reg.fit(X_in, y_in)
y_pred = lin_reg.predict(X_oof)
r2 = r2_score(y_oof, y_pred)
r2_scores.append(r2)
mse_score = mean_squared_error(y_oof, y_pred)
mse_scores.append(mse_score)
print('\t Variance score: \t{:.4f}'.format(r2))
print('\t Mean squared error: \t{:.4f}'.format(mse_score)) | code |
16164242/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
TEST_SIZE = 0.33
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
RANDOM_STATE = 123
N_SPLITS = 3
VERBOSE = False
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
def read_data(dataset='train', data_path=DATA_PATH):
"""
"""
index_col = None
index_type = ['train', 'test']
if dataset in index_type:
index_col = 'id'
data_path = csv_path(dataset, data_path=data_path)
return pd.read_csv(data_path, index_col=index_col)
train = read_data('train')
test = read_data('test')
molecule_train = pd.DataFrame({'molecule_name': train['molecule_name'].unique()})
molecule_test = pd.DataFrame({'molecule_name': test['molecule_name'].unique()})
structures = read_data('structures')
atom_list_df = structures.groupby('molecule_name')['atom'].apply(list)
atom_list_df = atom_list_df.to_frame()
if FREE_MEMORY:
del train
del test
molecule_train = pd.merge(molecule_train, atom_list_df, how='left', on='molecule_name')
molecule_test = pd.merge(molecule_test, atom_list_df, how='left', on='molecule_name')
potential_energy = read_data('potential_energy')
molecule_train = pd.merge(molecule_train, potential_energy)
if FREE_MEMORY:
del potential_energy
molecule_train.head() | code |
16164242/cell_2 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.linear_model import LinearRegression
import os
print(os.listdir('../input')) | code |
16164242/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
TEST_SIZE = 0.33
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
RANDOM_STATE = 123
N_SPLITS = 3
VERBOSE = False
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
def read_data(dataset='train', data_path=DATA_PATH):
"""
"""
index_col = None
index_type = ['train', 'test']
if dataset in index_type:
index_col = 'id'
data_path = csv_path(dataset, data_path=data_path)
return pd.read_csv(data_path, index_col=index_col)
train = read_data('train')
test = read_data('test')
molecule_train = pd.DataFrame({'molecule_name': train['molecule_name'].unique()})
molecule_test = pd.DataFrame({'molecule_name': test['molecule_name'].unique()})
structures = read_data('structures')
atom_list_df = structures.groupby('molecule_name')['atom'].apply(list)
atom_list_df = atom_list_df.to_frame()
if FREE_MEMORY:
del train
del test
molecule_train = pd.merge(molecule_train, atom_list_df, how='left', on='molecule_name')
molecule_test = pd.merge(molecule_test, atom_list_df, how='left', on='molecule_name')
potential_energy = read_data('potential_energy')
molecule_train = pd.merge(molecule_train, potential_energy)
if FREE_MEMORY:
del potential_energy
id_feature = 'molecule_name'
target_feature = (set(molecule_train) - set(molecule_test)).pop()
selected_features = list(molecule_test)
selected_features.remove(id_feature)
selected_features.remove('atom')
print('Selected Features: \t{}'.format(selected_features))
print('Target Feature: \t{}'.format(target_feature))
print('Id Feature: \t\t{}'.format(id_feature)) | code |
16164242/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
def read_data(dataset='train', data_path=DATA_PATH):
"""
"""
index_col = None
index_type = ['train', 'test']
if dataset in index_type:
index_col = 'id'
data_path = csv_path(dataset, data_path=data_path)
return pd.read_csv(data_path, index_col=index_col)
train = read_data('train')
test = read_data('test') | code |
16164242/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
TEST_SIZE = 0.33
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
RANDOM_STATE = 123
N_SPLITS = 3
VERBOSE = False
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
def read_data(dataset='train', data_path=DATA_PATH):
"""
"""
index_col = None
index_type = ['train', 'test']
if dataset in index_type:
index_col = 'id'
data_path = csv_path(dataset, data_path=data_path)
return pd.read_csv(data_path, index_col=index_col)
train = read_data('train')
test = read_data('test')
molecule_train = pd.DataFrame({'molecule_name': train['molecule_name'].unique()})
molecule_test = pd.DataFrame({'molecule_name': test['molecule_name'].unique()})
structures = read_data('structures')
atom_list_df = structures.groupby('molecule_name')['atom'].apply(list)
atom_list_df = atom_list_df.to_frame()
if FREE_MEMORY:
del train
del test
molecule_train = pd.merge(molecule_train, atom_list_df, how='left', on='molecule_name')
molecule_test = pd.merge(molecule_test, atom_list_df, how='left', on='molecule_name')
potential_energy = read_data('potential_energy')
molecule_train = pd.merge(molecule_train, potential_energy)
if FREE_MEMORY:
del potential_energy
plt.figure(figsize=FIGSIZE)
molecule_train['potential_energy'].plot(kind='kde')
plt.show() | code |
18154734/cell_9 | [
"image_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBunch.from_csv(LM_PATH, 'wiki_bangla_corpus.csv', text_cols='text')
learner = language_model_learner(data_lm, AWD_LSTM, pretrained=False, metrics=accuracy)
learner.lr_find()
learner.recorder.plot() | code |
18154734/cell_6 | [
"text_html_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBunch.from_csv(LM_PATH, 'wiki_bangla_corpus.csv', text_cols='text')
data_lm.show_batch() | code |
18154734/cell_2 | [
"image_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
print(len(LANG_FILENAMES))
LANG_FILENAMES[0:5] | code |
18154734/cell_11 | [
"text_html_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBunch.from_csv(LM_PATH, 'wiki_bangla_corpus.csv', text_cols='text')
learner = language_model_learner(data_lm, AWD_LSTM, pretrained=False, metrics=accuracy)
learner.lr_find()
learner.fit_one_cycle(15, 0.02)
learner.recorder.plot_losses() | code |
18154734/cell_8 | [
"image_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBunch.from_csv(LM_PATH, 'wiki_bangla_corpus.csv', text_cols='text')
learner = language_model_learner(data_lm, AWD_LSTM, pretrained=False, metrics=accuracy)
learner.lr_find() | code |
18154734/cell_10 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBunch.from_csv(LM_PATH, 'wiki_bangla_corpus.csv', text_cols='text')
learner = language_model_learner(data_lm, AWD_LSTM, pretrained=False, metrics=accuracy)
learner.lr_find()
learner.fit_one_cycle(15, 0.02) | code |
18154734/cell_12 | [
"text_plain_output_1.png"
] | from fastai.text import *
import html
import json
from sklearn.model_selection import train_test_split
BOS = 'xbos'
FLD = 'xfld'
PATH = Path('/kaggle/input/lolol/lolol')
LM_PATH = Path('/temp')
LM_PATH.mkdir(exist_ok=True)
LANG_FILENAMES = [str(f) for f in PATH.rglob('*/*')]
LANG_FILENAMES[0:5]
data_lm = TextLMDataBunch.from_csv(LM_PATH, 'wiki_bangla_corpus.csv', text_cols='text')
learner = language_model_learner(data_lm, AWD_LSTM, pretrained=False, metrics=accuracy)
learner.lr_find()
learner.fit_one_cycle(15, 0.02)
learner.recorder.plot_metrics() | code |
33105253/cell_9 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.info() | code |
33105253/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.head() | code |
33105253/cell_2 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33105253/cell_7 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"image_output_4.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.describe() | code |
33105253/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
def barPlot(feature):
temp = train[feature]
tempValue = temp.value_counts()
plt.xticks(tempValue.index, tempValue.index.values)
def plotHist(feature):
pass
numeric = ['Fare', 'Age', 'PassengerId']
for n in numeric:
plotHist(n) | code |
33105253/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
def barPlot(feature):
temp = train[feature]
tempValue = temp.value_counts()
plt.xticks(tempValue.index, tempValue.index.values)
category = ['Survived', 'Sex', 'Pclass', 'Embarked', 'SibSp', 'Parch']
for c in category:
barPlot(c) | code |
33105253/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns | code |
34121307/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.isnull().sum()
train.drop(['Dates', 'Category', 'Descript', 'DayOfWeek', 'Address'], inplace=True, axis=1)
test.drop(['Id', 'Dates', 'DayOfWeek', 'Address'], inplace=True, axis=1)
train.nunique()
from sklearn.preprocessing import LabelEncoder
le_PdDistrict = LabelEncoder()
le_Resolution = LabelEncoder()
le_X = LabelEncoder()
le_Y = LabelEncoder()
train['PdDistrict'] = le_PdDistrict.fit_transform(train['PdDistrict'])
train['Resolution'] = le_PdDistrict.fit_transform(train['Resolution'])
train['X'] = le_PdDistrict.fit_transform(train['X'])
train['Y'] = le_PdDistrict.fit_transform(train['Y'])
test['PdDistrict'] = le_PdDistrict.transform(test['PdDistrict'])
test['X'] = le_PdDistrict.transform(test['X'])
test['Y'] = le_PdDistrict.transform(test['Y']) | code |
34121307/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.head() | code |
34121307/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
test.info() | code |
34121307/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 |
34121307/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.isnull().sum()
train.drop(['Dates', 'Category', 'Descript', 'DayOfWeek', 'Address'], inplace=True, axis=1)
test.drop(['Id', 'Dates', 'DayOfWeek', 'Address'], inplace=True, axis=1)
train.nunique()
train.head() | code |
34121307/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.isnull().sum()
print(train['Resolution'].unique())
len(train['Resolution'].unique()) | code |
34121307/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
test.head() | code |
34121307/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('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.isnull().sum()
train.drop(['Dates', 'Category', 'Descript', 'DayOfWeek', 'Address'], inplace=True, axis=1)
test.drop(['Id', 'Dates', 'DayOfWeek', 'Address'], inplace=True, axis=1)
print(test.nunique())
train.nunique() | code |
34121307/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sf-crime/train.csv.zip')
test = pd.read_csv('/kaggle/input/sf-crime/test.csv.zip')
train.isnull().sum() | code |
1007790/cell_2 | [
"image_output_1.png"
] | from sklearn import cluster, datasets
import matplotlib.pyplot as plt
import numpy as np
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster, datasets
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.random.seed(0)
n_samples = 500
noisy_circles = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05)
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05)
blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)
no_structure = (np.random.rand(n_samples, 2), None)
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
clustering_names = ['MiniBatchKMeans', 'AffinityPropagation', 'MeanShift', 'SpectralClustering', 'Ward', 'AgglomerativeClustering', 'DBSCAN', 'Birch']
plot_num = 1
c = [[0, 0]]
a = [[0, 0]]
b = [[0, 0]]
d = [[0, 0]]
mu = 0.3
for x, y in [(0, 0.45), (0.9, 0.5), (0.45, 0.9), (0.45, 0)]:
num1 = np.random.normal(x, mu, n_samples)
num2 = np.random.normal(y, mu, n_samples)
nums = np.vstack((num1, num2)).T
a = np.vstack((a, nums))
for x, y in [(0, 0.2), (0.8, 0), (0.2, 1), (1, 0.8)]:
num1 = np.random.normal(x, mu, n_samples)
num2 = np.random.normal(y, mu, n_samples)
nums = np.vstack((num1, num2)).T
b = np.vstack((b, nums))
for x, y in [(0, 0), (0.9, 0.9), (0, 0.9), (0.9, 0)]:
num1 = np.random.normal(x, mu, n_samples)
num2 = np.random.normal(y, mu, n_samples)
nums = np.vstack((num1, num2)).T
c = np.vstack((c, nums))
for x, y in [(0, 0), (0.9, 0), (0.45, 0.779), (0.45, 0.259)]:
num1 = np.random.normal(x, mu, n_samples)
num2 = np.random.normal(y, mu, n_samples)
nums = np.vstack((num1, num2)).T
d = np.vstack((d, nums))
plt.scatter(d[:, 0], d[:, 1])
plt.show()
c = (c, None)
a = (a, None)
b = (b, None)
d = (d, None) | code |
1007790/cell_1 | [
"text_plain_output_1.png"
] | from sklearn import cluster, datasets
import matplotlib.pyplot as plt
import numpy as np
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster, datasets
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.random.seed(0)
n_samples = 500
noisy_circles = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05)
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05)
blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)
no_structure = (np.random.rand(n_samples, 2), None)
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
clustering_names = ['MiniBatchKMeans', 'AffinityPropagation', 'MeanShift', 'SpectralClustering', 'Ward', 'AgglomerativeClustering', 'DBSCAN', 'Birch']
plt.figure(figsize=(len(clustering_names) * 2 + 3, 9.5))
plt.subplots_adjust(left=0.02, right=0.98, bottom=0.001, top=0.96, wspace=0.05, hspace=0.01)
plot_num = 1 | code |
1007790/cell_3 | [
"image_output_1.png"
] | from sklearn import cluster, datasets
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import time
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster, datasets
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.random.seed(0)
n_samples = 500
noisy_circles = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05)
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05)
blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)
no_structure = (np.random.rand(n_samples, 2), None)
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
clustering_names = ['MiniBatchKMeans', 'AffinityPropagation', 'MeanShift', 'SpectralClustering', 'Ward', 'AgglomerativeClustering', 'DBSCAN', 'Birch']
plot_num = 1
c = [[0, 0]]
a = [[0, 0]]
b = [[0, 0]]
d = [[0, 0]]
mu = 0.3
for x, y in [(0, 0.45), (0.9, 0.5), (0.45, 0.9), (0.45, 0)]:
num1 = np.random.normal(x, mu, n_samples)
num2 = np.random.normal(y, mu, n_samples)
nums = np.vstack((num1, num2)).T
a = np.vstack((a, nums))
for x, y in [(0, 0.2), (0.8, 0), (0.2, 1), (1, 0.8)]:
num1 = np.random.normal(x, mu, n_samples)
num2 = np.random.normal(y, mu, n_samples)
nums = np.vstack((num1, num2)).T
b = np.vstack((b, nums))
for x, y in [(0, 0), (0.9, 0.9), (0, 0.9), (0.9, 0)]:
num1 = np.random.normal(x, mu, n_samples)
num2 = np.random.normal(y, mu, n_samples)
nums = np.vstack((num1, num2)).T
c = np.vstack((c, nums))
for x, y in [(0, 0), (0.9, 0), (0.45, 0.779), (0.45, 0.259)]:
num1 = np.random.normal(x, mu, n_samples)
num2 = np.random.normal(y, mu, n_samples)
nums = np.vstack((num1, num2)).T
d = np.vstack((d, nums))
c = (c, None)
a = (a, None)
b = (b, None)
d = (d, None)
datasets = [d, a, b, c]
for i_dataset, dataset in enumerate(datasets):
X, y = dataset
X = StandardScaler().fit_transform(X)
bandwidth = cluster.estimate_bandwidth(X, quantile=0.3)
connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)
connectivity = 0.5 * (connectivity + connectivity.T)
ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)
two_means = cluster.MiniBatchKMeans(n_clusters=4)
ward = cluster.AgglomerativeClustering(n_clusters=4, linkage='ward', connectivity=connectivity)
spectral = cluster.SpectralClustering(n_clusters=4, eigen_solver='arpack', affinity='nearest_neighbors')
dbscan = cluster.DBSCAN(eps=0.23, min_samples=30)
affinity_propagation = cluster.AffinityPropagation(damping=0.9, preference=-200)
average_linkage = cluster.AgglomerativeClustering(linkage='average', affinity='cityblock', n_clusters=4, connectivity=connectivity)
birch = cluster.Birch(n_clusters=4)
clustering_algorithms = [two_means, affinity_propagation, ms, spectral, ward, average_linkage, dbscan, birch]
for name, algorithm in zip(clustering_names, clustering_algorithms):
t0 = time.time()
algorithm.fit(X)
t1 = time.time()
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
else:
y_pred = algorithm.predict(X)
plt.subplot(4, len(clustering_algorithms), plot_num)
if i_dataset == 0:
plt.title(name, size=18)
plt.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), s=10)
if hasattr(algorithm, 'cluster_centers_'):
centers = algorithm.cluster_centers_
center_colors = colors[:len(centers)]
plt.scatter(centers[:, 0], centers[:, 1], s=100, c=center_colors)
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.xticks(())
plt.yticks(())
plt.text(0.99, 0.01, ('%.2fs' % (t1 - t0)).lstrip('0'), transform=plt.gca().transAxes, size=15, horizontalalignment='right')
plot_num += 1
plt.show() | code |
89127043/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train_df = pd.read_csv('../input/santander-customer-satisfaction/train.csv')
test_df = pd.read_csv('../input/santander-customer-satisfaction/test.csv')
sample_0 = np.where(train_df.TARGET == 0)[0]
sample_size = train_df.TARGET.value_counts()[1]
sample_loc = list(np.random.choice(sample_0, sample_size))
target_1_loc = list(np.where(train_df.TARGET == 1)[0])
sample_loc.extend(target_1_loc)
sample_loc.__len__() | code |
89127043/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train_df = pd.read_csv('../input/santander-customer-satisfaction/train.csv')
test_df = pd.read_csv('../input/santander-customer-satisfaction/test.csv')
plt.figure(figsize=(10, 10))
sns.barplot(x=train_df.TARGET.unique(), y=train_df.TARGET.value_counts(), palette='Pastel1') | code |
320432/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
full.Embarked.value_counts().plot(kind='bar') | code |
320432/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
titanic.CabinSide.value_counts() | code |
320432/cell_20 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
test[np.isnan(test['Fare'])]
full[np.isnan(full['Age'])].Title.unique() | code |
320432/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
titanic.info()
print('-' * 40)
test.info() | code |
320432/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
test.loc[test['PassengerId'] == 1044, 'Fare'] = full[(full['Embarked'] == 'S') & (full['Pclass'] == 3)].Fare.median()
test.loc[test['PassengerId'] == 1044, :]
test.loc[test['Name'].str.contains('Bowen,'), 'Cabin'] = 'B68'
test.loc[test.Cabin.str.len() == 5, :] | code |
320432/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
titanic.CabinSide.value_counts()
titanic.loc[titanic.Cabin.str.len() == 5, :]
titanic.Deck.value_counts() | code |
320432/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
full['Title'].value_counts() | code |
320432/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts() | code |
320432/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
full.head() | code |
320432/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
titanic.CabinSide.value_counts()
titanic.loc[titanic.Cabin.str.len() == 5, :]
titanic.Deck.value_counts()
titanic.loc[titanic['Deck'] == 'T', :] | code |
320432/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
test.loc[test['PassengerId'] == 1044, 'Fare'] = full[(full['Embarked'] == 'S') & (full['Pclass'] == 3)].Fare.median()
test.loc[test['PassengerId'] == 1044, :] | code |
320432/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
full[full['Title'] == 'Ms'] | code |
320432/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
full.Embarked.value_counts()
titanic.Embarked.fillna(value='S', inplace=True)
full.Embarked.fillna(value='S', inplace=True)
titanic.CabinSide.value_counts()
titanic.loc[titanic.Cabin.str.len() == 5, :] | code |
320432/cell_12 | [
"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)
titanic = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([titanic, test])
test[np.isnan(test['Fare'])] | code |
32062669/cell_21 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
gpu_status = torch.cuda.is_available()
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
class LeafDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.transform = transform
def __len__(self):
return self.df.shape[0]
def __getitem__(self, idx):
img_src = '../input/plant-pathology-2020-fgvc7/images/' + self.df.loc[idx, 'image_id'] + '.jpg'
image = PIL.Image.open(img_src).convert('RGB')
if self.transform:
image = self.transform(image)
if self.df.shape[1] == 5:
labels = self.df.loc[idx, ['healthy', 'multiple_diseases', 'rust', 'scab']].values
labels = torch.from_numpy(labels.astype(np.uint8))
labels = labels.unsqueeze(-1).long()
labels = labels.numpy().tolist().index([1])
labels = torch.from_numpy(np.asarray(labels))
return (image, labels)
else:
return image
leaf_sample_dataset = LeafDataset(df=train_df, transform=None)
fig, ax = plt.subplots(1,3)
for i in range(3):
img, label = leaf_sample_dataset[i]
ax[i].imshow(img)
print(type(img), img.size,label)
leaf_transform = transforms.Compose([transforms.Resize((512, 512)), transforms.CenterCrop((384, 384)), transforms.RandomAffine(degrees=15), transforms.RandomHorizontalFlip(p=0.4), transforms.RandomVerticalFlip(p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
leaf_train_dataset = LeafDataset(df=train_df, transform=leaf_transform)
leaf_train_loader = DataLoader(leaf_train_dataset, shuffle=True, batch_size=16)
images, labels = next(iter(leaf_train_loader))
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
leaf_test_dataset = LeafDataset(df=test_df, transform=leaf_transform)
leaf_test_loader = DataLoader(leaf_test_dataset, batch_size=64)
test_images = next(iter(leaf_test_loader))
print(len(leaf_test_dataset)) | code |
32062669/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
leaf_sample_dataset = LeafDataset(df=train_df, transform=None)
fig, ax = plt.subplots(1, 3)
for i in range(3):
img, label = leaf_sample_dataset[i]
ax[i].imshow(img)
print(type(img), img.size, label) | code |
32062669/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
train_df_copy.drop(['healthy', 'multiple_diseases', 'rust', 'scab'], axis=1, inplace=True)
train_df_copy.head(5) | code |
32062669/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
train_df.head(5) | code |
32062669/cell_23 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
gpu_status = torch.cuda.is_available()
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
class LeafDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.transform = transform
def __len__(self):
return self.df.shape[0]
def __getitem__(self, idx):
img_src = '../input/plant-pathology-2020-fgvc7/images/' + self.df.loc[idx, 'image_id'] + '.jpg'
image = PIL.Image.open(img_src).convert('RGB')
if self.transform:
image = self.transform(image)
if self.df.shape[1] == 5:
labels = self.df.loc[idx, ['healthy', 'multiple_diseases', 'rust', 'scab']].values
labels = torch.from_numpy(labels.astype(np.uint8))
labels = labels.unsqueeze(-1).long()
labels = labels.numpy().tolist().index([1])
labels = torch.from_numpy(np.asarray(labels))
return (image, labels)
else:
return image
leaf_sample_dataset = LeafDataset(df=train_df, transform=None)
fig, ax = plt.subplots(1,3)
for i in range(3):
img, label = leaf_sample_dataset[i]
ax[i].imshow(img)
print(type(img), img.size,label)
leaf_transform = transforms.Compose([transforms.Resize((512, 512)), transforms.CenterCrop((384, 384)), transforms.RandomAffine(degrees=15), transforms.RandomHorizontalFlip(p=0.4), transforms.RandomVerticalFlip(p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
leaf_train_dataset = LeafDataset(df=train_df, transform=leaf_transform)
leaf_train_loader = DataLoader(leaf_train_dataset, shuffle=True, batch_size=16)
images, labels = next(iter(leaf_train_loader))
dataset_size = len(leaf_train_dataset)
indices = list(range(dataset_size))
np.random.shuffle(indices)
split = int(np.floor(0.2 * dataset_size))
train_idx, val_idx = (indices[split:], indices[:split])
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(val_idx)
leaf_train_loader = DataLoader(leaf_train_dataset, sampler=train_sampler, batch_size=64)
leaf_valid_loader = DataLoader(leaf_train_dataset, sampler=valid_sampler, batch_size=64)
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
leaf_test_dataset = LeafDataset(df=test_df, transform=leaf_transform)
leaf_test_loader = DataLoader(leaf_test_dataset, batch_size=64)
test_images = next(iter(leaf_test_loader))
diagnosis = ['healthy', 'multiple_diseases', 'rust', 'scab']
train_images, train_labels = next(iter(leaf_train_loader))
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(8):
ax = fig.add_subplot(2, 16 / 2, idx + 1, xticks=[], yticks=[])
plt.imshow(train_images[idx].numpy().transpose(1, 2, 0))
ax.set_title(diagnosis[labels[idx]]) | code |
32062669/cell_30 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import datetime
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
gpu_status = torch.cuda.is_available()
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
class LeafDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.transform = transform
def __len__(self):
return self.df.shape[0]
def __getitem__(self, idx):
img_src = '../input/plant-pathology-2020-fgvc7/images/' + self.df.loc[idx, 'image_id'] + '.jpg'
image = PIL.Image.open(img_src).convert('RGB')
if self.transform:
image = self.transform(image)
if self.df.shape[1] == 5:
labels = self.df.loc[idx, ['healthy', 'multiple_diseases', 'rust', 'scab']].values
labels = torch.from_numpy(labels.astype(np.uint8))
labels = labels.unsqueeze(-1).long()
labels = labels.numpy().tolist().index([1])
labels = torch.from_numpy(np.asarray(labels))
return (image, labels)
else:
return image
leaf_sample_dataset = LeafDataset(df=train_df, transform=None)
fig, ax = plt.subplots(1,3)
for i in range(3):
img, label = leaf_sample_dataset[i]
ax[i].imshow(img)
print(type(img), img.size,label)
leaf_transform = transforms.Compose([transforms.Resize((512, 512)), transforms.CenterCrop((384, 384)), transforms.RandomAffine(degrees=15), transforms.RandomHorizontalFlip(p=0.4), transforms.RandomVerticalFlip(p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
leaf_train_dataset = LeafDataset(df=train_df, transform=leaf_transform)
leaf_train_loader = DataLoader(leaf_train_dataset, shuffle=True, batch_size=16)
images, labels = next(iter(leaf_train_loader))
dataset_size = len(leaf_train_dataset)
indices = list(range(dataset_size))
np.random.shuffle(indices)
split = int(np.floor(0.2 * dataset_size))
train_idx, val_idx = (indices[split:], indices[:split])
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(val_idx)
leaf_train_loader = DataLoader(leaf_train_dataset, sampler=train_sampler, batch_size=64)
leaf_valid_loader = DataLoader(leaf_train_dataset, sampler=valid_sampler, batch_size=64)
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
leaf_test_dataset = LeafDataset(df=test_df, transform=leaf_transform)
leaf_test_loader = DataLoader(leaf_test_dataset, batch_size=64)
test_images = next(iter(leaf_test_loader))
diagnosis = ['healthy', 'multiple_diseases', 'rust', 'scab']
train_images, train_labels = next(iter(leaf_train_loader))
fig = plt.figure(figsize=(25,4))
for idx in np.arange(8):
ax = fig.add_subplot(2, 16/2, idx+1, xticks=[], yticks=[])
plt.imshow(train_images[idx].numpy().transpose(1,2,0))
ax.set_title(diagnosis[labels[idx]])
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 8, 3, padding=1)
self.conv2 = nn.Conv2d(8, 16, 3, padding=1)
self.conv3 = nn.Conv2d(16, 32, 3, padding=1)
self.conv4 = nn.Conv2d(32, 64, 3, padding=1)
self.conv5 = nn.Conv2d(64, 128, 3, padding=1)
self.conv6 = nn.Conv2d(128, 256, 2, padding=1)
self.conv7 = nn.Conv2d(256, 512, 2, padding=1)
self.pool2 = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(12 * 12 * 512, 2048)
self.fc2 = nn.Linear(2048, 4)
self.dropout = nn.Dropout(0.2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool2(F.relu(self.conv2(x)))
x = F.relu(self.conv3(x))
x = self.pool2(F.relu(self.conv4(x)))
x = self.pool2(F.relu(self.conv5(x)))
x = self.pool2(F.relu(self.conv6(x)))
x = self.pool2(F.relu(self.conv7(x)))
x = x.view(-1, 12 * 12 * 512)
x = self.dropout(x)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
model = Net()
if gpu_status:
model.cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.0008)
no_epochs = 40
valid_loss_min = np.Inf
curr_time = datetime.datetime.now()
curr_timestamp = str(datetime.datetime.now())
for epoch in range(1, no_epochs + 1):
train_loss = 0.0
valid_loss = 0.0
model.train()
for data, target in leaf_train_loader:
if gpu_status:
data = data.cuda()
target = target.cuda()
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss += loss.item() * data.size(0)
model.eval()
for data, target in leaf_valid_loader:
if gpu_status:
data = data.cuda()
target = target.cuda()
output = model(data)
loss = criterion(output, target)
valid_loss += loss.item() * data.size(0)
train_loss = train_loss / len(leaf_train_loader.dataset)
valid_loss = valid_loss / len(leaf_valid_loader.dataset)
if valid_loss < valid_loss_min:
torch.save(model.state_dict(), 'Kaggle_kernel_model_apple_leaf' + curr_timestamp + '.pt')
valid_loss_min = valid_loss
file_name = 'Kaggle_kernel_model_apple_leaf' + str(curr_timestamp)
model.load_state_dict(torch.load(file_name + '.pt')) | code |
32062669/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
gpu_status = torch.cuda.is_available()
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
class LeafDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.transform = transform
def __len__(self):
return self.df.shape[0]
def __getitem__(self, idx):
img_src = '../input/plant-pathology-2020-fgvc7/images/' + self.df.loc[idx, 'image_id'] + '.jpg'
image = PIL.Image.open(img_src).convert('RGB')
if self.transform:
image = self.transform(image)
if self.df.shape[1] == 5:
labels = self.df.loc[idx, ['healthy', 'multiple_diseases', 'rust', 'scab']].values
labels = torch.from_numpy(labels.astype(np.uint8))
labels = labels.unsqueeze(-1).long()
labels = labels.numpy().tolist().index([1])
labels = torch.from_numpy(np.asarray(labels))
return (image, labels)
else:
return image
leaf_sample_dataset = LeafDataset(df=train_df, transform=None)
fig, ax = plt.subplots(1,3)
for i in range(3):
img, label = leaf_sample_dataset[i]
ax[i].imshow(img)
print(type(img), img.size,label)
leaf_transform = transforms.Compose([transforms.Resize((512, 512)), transforms.CenterCrop((384, 384)), transforms.RandomAffine(degrees=15), transforms.RandomHorizontalFlip(p=0.4), transforms.RandomVerticalFlip(p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
leaf_train_dataset = LeafDataset(df=train_df, transform=leaf_transform)
leaf_train_loader = DataLoader(leaf_train_dataset, shuffle=True, batch_size=16)
images, labels = next(iter(leaf_train_loader))
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
leaf_test_dataset = LeafDataset(df=test_df, transform=leaf_transform)
leaf_test_loader = DataLoader(leaf_test_dataset, batch_size=64)
test_images = next(iter(leaf_test_loader))
print(len(test_images))
print(test_images[0].shape)
plt.imshow(test_images[2].numpy().transpose((1, 2, 0))) | code |
32062669/cell_29 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
test_df.head(5) | code |
32062669/cell_26 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import datetime
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
gpu_status = torch.cuda.is_available()
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
class LeafDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.transform = transform
def __len__(self):
return self.df.shape[0]
def __getitem__(self, idx):
img_src = '../input/plant-pathology-2020-fgvc7/images/' + self.df.loc[idx, 'image_id'] + '.jpg'
image = PIL.Image.open(img_src).convert('RGB')
if self.transform:
image = self.transform(image)
if self.df.shape[1] == 5:
labels = self.df.loc[idx, ['healthy', 'multiple_diseases', 'rust', 'scab']].values
labels = torch.from_numpy(labels.astype(np.uint8))
labels = labels.unsqueeze(-1).long()
labels = labels.numpy().tolist().index([1])
labels = torch.from_numpy(np.asarray(labels))
return (image, labels)
else:
return image
leaf_sample_dataset = LeafDataset(df=train_df, transform=None)
fig, ax = plt.subplots(1,3)
for i in range(3):
img, label = leaf_sample_dataset[i]
ax[i].imshow(img)
print(type(img), img.size,label)
leaf_transform = transforms.Compose([transforms.Resize((512, 512)), transforms.CenterCrop((384, 384)), transforms.RandomAffine(degrees=15), transforms.RandomHorizontalFlip(p=0.4), transforms.RandomVerticalFlip(p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
leaf_train_dataset = LeafDataset(df=train_df, transform=leaf_transform)
leaf_train_loader = DataLoader(leaf_train_dataset, shuffle=True, batch_size=16)
images, labels = next(iter(leaf_train_loader))
dataset_size = len(leaf_train_dataset)
indices = list(range(dataset_size))
np.random.shuffle(indices)
split = int(np.floor(0.2 * dataset_size))
train_idx, val_idx = (indices[split:], indices[:split])
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(val_idx)
leaf_train_loader = DataLoader(leaf_train_dataset, sampler=train_sampler, batch_size=64)
leaf_valid_loader = DataLoader(leaf_train_dataset, sampler=valid_sampler, batch_size=64)
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
leaf_test_dataset = LeafDataset(df=test_df, transform=leaf_transform)
leaf_test_loader = DataLoader(leaf_test_dataset, batch_size=64)
test_images = next(iter(leaf_test_loader))
diagnosis = ['healthy', 'multiple_diseases', 'rust', 'scab']
train_images, train_labels = next(iter(leaf_train_loader))
fig = plt.figure(figsize=(25,4))
for idx in np.arange(8):
ax = fig.add_subplot(2, 16/2, idx+1, xticks=[], yticks=[])
plt.imshow(train_images[idx].numpy().transpose(1,2,0))
ax.set_title(diagnosis[labels[idx]])
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 8, 3, padding=1)
self.conv2 = nn.Conv2d(8, 16, 3, padding=1)
self.conv3 = nn.Conv2d(16, 32, 3, padding=1)
self.conv4 = nn.Conv2d(32, 64, 3, padding=1)
self.conv5 = nn.Conv2d(64, 128, 3, padding=1)
self.conv6 = nn.Conv2d(128, 256, 2, padding=1)
self.conv7 = nn.Conv2d(256, 512, 2, padding=1)
self.pool2 = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(12 * 12 * 512, 2048)
self.fc2 = nn.Linear(2048, 4)
self.dropout = nn.Dropout(0.2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool2(F.relu(self.conv2(x)))
x = F.relu(self.conv3(x))
x = self.pool2(F.relu(self.conv4(x)))
x = self.pool2(F.relu(self.conv5(x)))
x = self.pool2(F.relu(self.conv6(x)))
x = self.pool2(F.relu(self.conv7(x)))
x = x.view(-1, 12 * 12 * 512)
x = self.dropout(x)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
model = Net()
if gpu_status:
model.cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.0008)
no_epochs = 40
valid_loss_min = np.Inf
curr_time = datetime.datetime.now()
curr_timestamp = str(datetime.datetime.now())
for epoch in range(1, no_epochs + 1):
train_loss = 0.0
valid_loss = 0.0
model.train()
for data, target in leaf_train_loader:
if gpu_status:
data = data.cuda()
target = target.cuda()
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss += loss.item() * data.size(0)
model.eval()
for data, target in leaf_valid_loader:
if gpu_status:
data = data.cuda()
target = target.cuda()
output = model(data)
loss = criterion(output, target)
valid_loss += loss.item() * data.size(0)
train_loss = train_loss / len(leaf_train_loader.dataset)
valid_loss = valid_loss / len(leaf_valid_loader.dataset)
print(datetime.datetime.now() - curr_time)
print('Epoch {}: Training Loss : {:.4f} Validation Loss : {:.4f}'.format(epoch, train_loss, valid_loss))
if valid_loss < valid_loss_min:
print('Validation loss decreased {:.6f} -> {:.6f}, Saving model...'.format(valid_loss_min, valid_loss))
torch.save(model.state_dict(), 'Kaggle_kernel_model_apple_leaf' + curr_timestamp + '.pt')
valid_loss_min = valid_loss | code |
32062669/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import torch
gpu_status = torch.cuda.is_available()
if not gpu_status:
print('No GPU, Using CPU')
else:
print('Using GPU') | code |
32062669/cell_11 | [
"text_html_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_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
print(len(train_df))
print(train_df.shape[1]) | code |
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