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104124186/cell_8 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
plt.scatter(a, b)
plt.scatter(c, d)
plt.grid()
plt.show() | code |
104124186/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df
x = df.YearsExperience.values.reshape(-1, 1)
y = df.Salary.values.reshape(-1, 1)
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
compare = pd.DataFrame({'Actual': y_test.flatten(), 'Prediction': y_pred.flatten()})
compare
df2 = pd.DataFrame({'YearsExperience': [1.5, 2.5, 3.5, 4.5, 5], 'Salary': [1, 4, 8, 9, 10]})
df3 = df.append(df2)
df3 | code |
104124186/cell_3 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df | code |
104124186/cell_24 | [
"text_html_output_1.png"
] | YearsExperience = float(input('please enter the years expercience: '))
Salary = 26986.69131674 + 9379.71049195 * YearsExperience
print(Salary) | code |
104124186/cell_14 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
compare = pd.DataFrame({'Actual': y_test.flatten(), 'Prediction': y_pred.flatten()})
compare
a = x_train
b = y_train
c = x_test
d = y_pred
plt.scatter(a, b)
plt.scatter(c, d)
plt.grid()
plt.show() | code |
104124186/cell_22 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df
x = df.YearsExperience.values.reshape(-1, 1)
y = df.Salary.values.reshape(-1, 1)
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
compare = pd.DataFrame({'Actual': y_test.flatten(), 'Prediction': y_pred.flatten()})
compare
a = x_train
b = y_train
c = x_test
d = y_pred
df2 = pd.DataFrame({'YearsExperience': [1.5, 2.5, 3.5, 4.5, 5], 'Salary': [1, 4, 8, 9, 10]})
df3 = df.append(df2)
df3
train = df3.iloc[:25]
test = df3.iloc[25:]
x_train = df3['YearsExperience'][:30].values.reshape(-1, 1)
y_train = df3['Salary'][:30].values.reshape(-1, 1)
x_test = df3['YearsExperience'][30:].values.reshape(-1, 1)
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
a = x_train
b = y_train
c = x_test
d = y_pred
plt.scatter(a, b)
plt.scatter(c, d)
plt.grid()
plt.show() | code |
104124186/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
print(regressor.intercept_)
print(regressor.coef_) | code |
104124186/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
plt.scatter(y_test, y_pred)
plt.show() | code |
128000238/cell_2 | [
"text_plain_output_1.png"
] | !pip install azure-ai-textanalytics --pre | code |
128000238/cell_7 | [
"text_plain_output_1.png"
] | def create_twitter_url():
handle = 'nasi goreng'
max_results = 10
mrf = 'max_results={}'.format(max_results)
q = 'query={}'.format(handle)
url = 'https://api.twitter.com/2/tweets/search/recent?{}&{}'.format(mrf, q)
return url
create_twitter_url() | code |
128000238/cell_16 | [
"text_plain_output_1.png"
] | from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential
from kaggle_secrets import UserSecretsClient
import ast
import json
import requests
import requests
import json
import ast
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient
from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient()
azure_endpoint = user_secrets.get_secret('AZURE_LANGUAGE_ENDPOINT')
azure_language_key = user_secrets.get_secret('AZURE_LANGUAGE_KEY')
bearer_token = user_secrets.get_secret('bearer_token')
def create_twitter_url():
handle = 'nasi goreng'
max_results = 10
mrf = 'max_results={}'.format(max_results)
q = 'query={}'.format(handle)
url = 'https://api.twitter.com/2/tweets/search/recent?{}&{}'.format(mrf, q)
return url
def twitter_auth_and_connect(bearer_token, url):
headers = {'Authorization': 'Bearer {}'.format(bearer_token)}
response = requests.request('GET', url, headers=headers)
return response.json()
def lang_data_shape(res_json):
data_only = res_json['data']
doc_start = '"documents": {}'.format(data_only)
str_json = '{' + doc_start + '}'
dump_doc = json.dumps(str_json)
doc = json.loads(dump_doc)
return ast.literal_eval(doc)
def main():
url = create_twitter_url()
res_json = twitter_auth_and_connect(bearer_token, url)
documents = lang_data_shape(res_json)
text_analytics_client = TextAnalyticsClient(endpoint=azure_endpoint, credential=AzureKeyCredential(azure_language_key))
result = text_analytics_client.analyze_sentiment(documents['documents'], show_opinion_mining=False)
doc_result = [doc for doc in result if not doc.is_error]
for document in doc_result:
print('\n')
print(document.sentiment)
print('=======')
for sentence in document.sentences:
print(sentence.text)
if __name__ == '__main__':
main() | code |
17136911/cell_9 | [
"text_plain_output_1.png"
] | from keras import layers
from keras.optimizers import Adam
from keras.layers import Input, Dense, Activation, BatchNormalization, Flatten
from keras.layers import Dropout
from keras.models import Model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from keras.models import Sequential
from keras.layers import Bidirectional
from keras.layers import LSTM | code |
17136911/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Bidirectional
from keras.layers import Input, Dense, Activation, BatchNormalization, Flatten
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
le = LabelEncoder()
oh = OneHotEncoder(sparse=False)
df = pd.read_csv('../input/train.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index_0', 'atom_index_1', 'type', 'scalar_coupling_constant'])
df_m_coupling_contributions = pd.read_csv('../input/scalar_coupling_contributions.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index_0', 'atom_index_1', 'fc', 'sd', 'pso', 'dso'])
df = pd.merge(df, df_m_coupling_contributions, on=['molecule_name', 'atom_index_0', 'atom_index_1'])
df_m_diple_moments = pd.read_csv('../input/dipole_moments.csv', sep=',', header=0, usecols=['molecule_name', 'X', 'Y', 'Z'])
df = pd.merge(df, df_m_diple_moments, on='molecule_name')
df_m_pot_engy = pd.read_csv('../input/potential_energy.csv', sep=',', header=0, usecols=['molecule_name', 'potential_energy'])
df = pd.merge(df, df_m_pot_engy, on='molecule_name')
df_a_str = pd.read_csv('../input/structures.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'atom', 'x', 'y', 'z'])
f = df_a_str['atom'].values
f = np.reshape(f, (-1, 1))
f = oh.fit_transform(f)
ohdf = pd.DataFrame(f)
df_a_str = pd.concat([df_a_str, ohdf], axis=1)
df_a_str = df_a_str.rename(index=str, columns={0: 'A0', 1: 'A1', 2: 'A2', 3: 'A3', 4: 'A4'})
df_a_str.drop(columns=['atom'], inplace=True)
df_a_mag_sh_tensor = pd.read_csv('../input/magnetic_shielding_tensors.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'XX', 'YX', 'ZX', 'XY', 'YY', 'ZY', 'XZ', 'YZ', 'ZZ'])
df_a_mlkn_charges = pd.read_csv('../input/mulliken_charges.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'mulliken_charge'])
df_a_str = pd.merge(df_a_str, df_a_mag_sh_tensor, on=['molecule_name', 'atom_index'])
df_a_str = pd.merge(df_a_str, df_a_mlkn_charges, on=['molecule_name', 'atom_index'])
df_atom_1_prop = df_a_str.rename(index=str, columns={'atom_index': 'atom_index_0', 'A0': 'A0_0', 'A1': 'A1_0', 'A2': 'A2_0', 'A3': 'A3_0', 'A4': 'A4_0', 'x': 'x_0', 'y': 'y_0', 'z': 'z_0', 'XX': 'XX_0', 'YX': 'YX_0', 'ZX': 'ZX_0', 'XY': 'XY_0', 'YY': 'YY_0', 'ZY': 'ZY_0', 'XZ': 'XZ_0', 'YZ': 'YZ_0', 'ZZ': 'ZZ_0', 'mulliken_charge': 'mulliken_charge_0'})
df = pd.merge(df, df_atom_1_prop, on=['molecule_name', 'atom_index_0'])
df_atom_2_prop = df_a_str.rename(index=str, columns={'atom_index': 'atom_index_1', 'A0': 'A0_1', 'A1': 'A1_1', 'A2': 'A2_1', 'A3': 'A3_1', 'A4': 'A4_1', 'x': 'x_1', 'y': 'y_1', 'z': 'z_1', 'XX': 'XX_1', 'YX': 'YX_1', 'ZX': 'ZX_1', 'XY': 'XY_1', 'YY': 'YY_1', 'ZY': 'ZY_1', 'XZ': 'XZ_1', 'YZ': 'YZ_1', 'ZZ': 'ZZ_1', 'mulliken_charge': 'mulliken_charge_1'})
df = pd.merge(df, df_atom_2_prop, on=['molecule_name', 'atom_index_1'])
ss = StandardScaler()
df[['scalar_coupling_constant', 'fc', 'sd', 'pso', 'dso', 'X', 'Y', 'Z', 'potential_energy', 'x_0', 'y_0', 'z_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'x_1', 'y_1', 'z_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1']] = ss.fit_transform(df[['scalar_coupling_constant', 'fc', 'sd', 'pso', 'dso', 'X', 'Y', 'Z', 'potential_energy', 'x_0', 'y_0', 'z_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'x_1', 'y_1', 'z_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1']])
def build_atom_pairs(name, molecule):
df = molecule.apply(list)
atom_pair_y = np.zeros((df.shape[0], 8))
atom_pair = np.zeros((df.shape[0], 2, 18))
atom_pair[:, 0, :] = df.as_matrix(columns=['x_0', 'y_0', 'z_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0'])
atom_pair[:, 1, :] = df.as_matrix(columns=['x_1', 'y_1', 'z_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1'])
atom_pair_y = df.as_matrix(columns=['potential_energy', 'X', 'Y', 'Z', 'fc', 'sd', 'pso', 'dso'])
return (atom_pair, atom_pair_y)
moleculelist = []
molecule_ylist = []
molecules = df.groupby('molecule_name')
c = 0
for name, molecule in molecules:
atoms, molecule_y = build_atom_pairs(name, molecule)
amolecule = np.zeros((650, atoms.shape[1], atoms.shape[2]))
amolecule[:atoms.shape[0], :atoms.shape[1], :atoms.shape[2]] = atoms
amolecule = amolecule.transpose([0, 2, 1]).reshape(amolecule.shape[0], -1)
amolecule_y = np.zeros((650, molecule_y.shape[1]))
amolecule_y[:molecule_y.shape[0], :molecule_y.shape[1]] = molecule_y
moleculelist.append(amolecule)
molecule_ylist.append(amolecule_y)
c = c + 1
if c > 10000:
break
def BRNNModel(inputdim):
model = Sequential()
model.add(Bidirectional(LSTM(100, return_sequences=True, input_dim=inputdim)))
model.add(Dense(8))
model.add(Activation('relu'))
return model
def batch_generator(X, y, batch_size):
number_of_batches = X.shape[0] / batch_size
counter = 0
shuffle_index = np.arange(np.shape(y)[0])
while 1:
index_batch = shuffle_index[batch_size * counter:batch_size * (counter + 1)]
X_batch = X[index_batch, :].todense()
y_batch = y[index_batch]
counter += 1
yield (np.array(X_batch), y_batch)
if counter > number_of_batches:
counter = 0
X = np.asarray(moleculelist)
y = np.asarray(molecule_ylist)
model = BRNNModel(X.shape[1])
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
model.fit(X_train, Y_train, epochs=30, batch_size=16, verbose=2)
preds = model.evaluate(x=X_test, y=Y_test)
print()
print('Loss = ' + str(preds[0]))
print('Test Accuracy = ' + str(preds[1])) | code |
17136911/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
import os
print(os.listdir('../input')) | code |
17136911/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
le = LabelEncoder()
oh = OneHotEncoder(sparse=False)
df = pd.read_csv('../input/train.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index_0', 'atom_index_1', 'type', 'scalar_coupling_constant'])
df_m_coupling_contributions = pd.read_csv('../input/scalar_coupling_contributions.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index_0', 'atom_index_1', 'fc', 'sd', 'pso', 'dso'])
df = pd.merge(df, df_m_coupling_contributions, on=['molecule_name', 'atom_index_0', 'atom_index_1'])
df_m_diple_moments = pd.read_csv('../input/dipole_moments.csv', sep=',', header=0, usecols=['molecule_name', 'X', 'Y', 'Z'])
df = pd.merge(df, df_m_diple_moments, on='molecule_name')
df_m_pot_engy = pd.read_csv('../input/potential_energy.csv', sep=',', header=0, usecols=['molecule_name', 'potential_energy'])
df = pd.merge(df, df_m_pot_engy, on='molecule_name')
df_a_str = pd.read_csv('../input/structures.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'atom', 'x', 'y', 'z'])
f = df_a_str['atom'].values
f = np.reshape(f, (-1, 1))
f = oh.fit_transform(f)
ohdf = pd.DataFrame(f)
df_a_str = pd.concat([df_a_str, ohdf], axis=1)
df_a_str = df_a_str.rename(index=str, columns={0: 'A0', 1: 'A1', 2: 'A2', 3: 'A3', 4: 'A4'})
df_a_str.drop(columns=['atom'], inplace=True)
df_a_mag_sh_tensor = pd.read_csv('../input/magnetic_shielding_tensors.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'XX', 'YX', 'ZX', 'XY', 'YY', 'ZY', 'XZ', 'YZ', 'ZZ'])
df_a_mlkn_charges = pd.read_csv('../input/mulliken_charges.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'mulliken_charge'])
df_a_str = pd.merge(df_a_str, df_a_mag_sh_tensor, on=['molecule_name', 'atom_index'])
df_a_str = pd.merge(df_a_str, df_a_mlkn_charges, on=['molecule_name', 'atom_index'])
df_atom_1_prop = df_a_str.rename(index=str, columns={'atom_index': 'atom_index_0', 'A0': 'A0_0', 'A1': 'A1_0', 'A2': 'A2_0', 'A3': 'A3_0', 'A4': 'A4_0', 'x': 'x_0', 'y': 'y_0', 'z': 'z_0', 'XX': 'XX_0', 'YX': 'YX_0', 'ZX': 'ZX_0', 'XY': 'XY_0', 'YY': 'YY_0', 'ZY': 'ZY_0', 'XZ': 'XZ_0', 'YZ': 'YZ_0', 'ZZ': 'ZZ_0', 'mulliken_charge': 'mulliken_charge_0'})
df = pd.merge(df, df_atom_1_prop, on=['molecule_name', 'atom_index_0'])
df_atom_2_prop = df_a_str.rename(index=str, columns={'atom_index': 'atom_index_1', 'A0': 'A0_1', 'A1': 'A1_1', 'A2': 'A2_1', 'A3': 'A3_1', 'A4': 'A4_1', 'x': 'x_1', 'y': 'y_1', 'z': 'z_1', 'XX': 'XX_1', 'YX': 'YX_1', 'ZX': 'ZX_1', 'XY': 'XY_1', 'YY': 'YY_1', 'ZY': 'ZY_1', 'XZ': 'XZ_1', 'YZ': 'YZ_1', 'ZZ': 'ZZ_1', 'mulliken_charge': 'mulliken_charge_1'})
df = pd.merge(df, df_atom_2_prop, on=['molecule_name', 'atom_index_1'])
ss = StandardScaler()
df[['scalar_coupling_constant', 'fc', 'sd', 'pso', 'dso', 'X', 'Y', 'Z', 'potential_energy', 'x_0', 'y_0', 'z_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'x_1', 'y_1', 'z_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1']] = ss.fit_transform(df[['scalar_coupling_constant', 'fc', 'sd', 'pso', 'dso', 'X', 'Y', 'Z', 'potential_energy', 'x_0', 'y_0', 'z_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'x_1', 'y_1', 'z_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1']])
def build_atom_pairs(name, molecule):
df = molecule.apply(list)
atom_pair_y = np.zeros((df.shape[0], 8))
atom_pair = np.zeros((df.shape[0], 2, 18))
atom_pair[:, 0, :] = df.as_matrix(columns=['x_0', 'y_0', 'z_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0'])
atom_pair[:, 1, :] = df.as_matrix(columns=['x_1', 'y_1', 'z_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1'])
atom_pair_y = df.as_matrix(columns=['potential_energy', 'X', 'Y', 'Z', 'fc', 'sd', 'pso', 'dso'])
return (atom_pair, atom_pair_y)
moleculelist = []
molecule_ylist = []
molecules = df.groupby('molecule_name')
c = 0
for name, molecule in molecules:
atoms, molecule_y = build_atom_pairs(name, molecule)
amolecule = np.zeros((650, atoms.shape[1], atoms.shape[2]))
amolecule[:atoms.shape[0], :atoms.shape[1], :atoms.shape[2]] = atoms
amolecule = amolecule.transpose([0, 2, 1]).reshape(amolecule.shape[0], -1)
amolecule_y = np.zeros((650, molecule_y.shape[1]))
amolecule_y[:molecule_y.shape[0], :molecule_y.shape[1]] = molecule_y
moleculelist.append(amolecule)
molecule_ylist.append(amolecule_y)
c = c + 1
if c > 10000:
break | code |
17136911/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Bidirectional
from keras.layers import Input, Dense, Activation, BatchNormalization, Flatten
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
le = LabelEncoder()
oh = OneHotEncoder(sparse=False)
df = pd.read_csv('../input/train.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index_0', 'atom_index_1', 'type', 'scalar_coupling_constant'])
df_m_coupling_contributions = pd.read_csv('../input/scalar_coupling_contributions.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index_0', 'atom_index_1', 'fc', 'sd', 'pso', 'dso'])
df = pd.merge(df, df_m_coupling_contributions, on=['molecule_name', 'atom_index_0', 'atom_index_1'])
df_m_diple_moments = pd.read_csv('../input/dipole_moments.csv', sep=',', header=0, usecols=['molecule_name', 'X', 'Y', 'Z'])
df = pd.merge(df, df_m_diple_moments, on='molecule_name')
df_m_pot_engy = pd.read_csv('../input/potential_energy.csv', sep=',', header=0, usecols=['molecule_name', 'potential_energy'])
df = pd.merge(df, df_m_pot_engy, on='molecule_name')
df_a_str = pd.read_csv('../input/structures.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'atom', 'x', 'y', 'z'])
f = df_a_str['atom'].values
f = np.reshape(f, (-1, 1))
f = oh.fit_transform(f)
ohdf = pd.DataFrame(f)
df_a_str = pd.concat([df_a_str, ohdf], axis=1)
df_a_str = df_a_str.rename(index=str, columns={0: 'A0', 1: 'A1', 2: 'A2', 3: 'A3', 4: 'A4'})
df_a_str.drop(columns=['atom'], inplace=True)
df_a_mag_sh_tensor = pd.read_csv('../input/magnetic_shielding_tensors.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'XX', 'YX', 'ZX', 'XY', 'YY', 'ZY', 'XZ', 'YZ', 'ZZ'])
df_a_mlkn_charges = pd.read_csv('../input/mulliken_charges.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'mulliken_charge'])
df_a_str = pd.merge(df_a_str, df_a_mag_sh_tensor, on=['molecule_name', 'atom_index'])
df_a_str = pd.merge(df_a_str, df_a_mlkn_charges, on=['molecule_name', 'atom_index'])
df_atom_1_prop = df_a_str.rename(index=str, columns={'atom_index': 'atom_index_0', 'A0': 'A0_0', 'A1': 'A1_0', 'A2': 'A2_0', 'A3': 'A3_0', 'A4': 'A4_0', 'x': 'x_0', 'y': 'y_0', 'z': 'z_0', 'XX': 'XX_0', 'YX': 'YX_0', 'ZX': 'ZX_0', 'XY': 'XY_0', 'YY': 'YY_0', 'ZY': 'ZY_0', 'XZ': 'XZ_0', 'YZ': 'YZ_0', 'ZZ': 'ZZ_0', 'mulliken_charge': 'mulliken_charge_0'})
df = pd.merge(df, df_atom_1_prop, on=['molecule_name', 'atom_index_0'])
df_atom_2_prop = df_a_str.rename(index=str, columns={'atom_index': 'atom_index_1', 'A0': 'A0_1', 'A1': 'A1_1', 'A2': 'A2_1', 'A3': 'A3_1', 'A4': 'A4_1', 'x': 'x_1', 'y': 'y_1', 'z': 'z_1', 'XX': 'XX_1', 'YX': 'YX_1', 'ZX': 'ZX_1', 'XY': 'XY_1', 'YY': 'YY_1', 'ZY': 'ZY_1', 'XZ': 'XZ_1', 'YZ': 'YZ_1', 'ZZ': 'ZZ_1', 'mulliken_charge': 'mulliken_charge_1'})
df = pd.merge(df, df_atom_2_prop, on=['molecule_name', 'atom_index_1'])
ss = StandardScaler()
df[['scalar_coupling_constant', 'fc', 'sd', 'pso', 'dso', 'X', 'Y', 'Z', 'potential_energy', 'x_0', 'y_0', 'z_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'x_1', 'y_1', 'z_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1']] = ss.fit_transform(df[['scalar_coupling_constant', 'fc', 'sd', 'pso', 'dso', 'X', 'Y', 'Z', 'potential_energy', 'x_0', 'y_0', 'z_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'x_1', 'y_1', 'z_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1']])
def build_atom_pairs(name, molecule):
df = molecule.apply(list)
atom_pair_y = np.zeros((df.shape[0], 8))
atom_pair = np.zeros((df.shape[0], 2, 18))
atom_pair[:, 0, :] = df.as_matrix(columns=['x_0', 'y_0', 'z_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0'])
atom_pair[:, 1, :] = df.as_matrix(columns=['x_1', 'y_1', 'z_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1'])
atom_pair_y = df.as_matrix(columns=['potential_energy', 'X', 'Y', 'Z', 'fc', 'sd', 'pso', 'dso'])
return (atom_pair, atom_pair_y)
moleculelist = []
molecule_ylist = []
molecules = df.groupby('molecule_name')
c = 0
for name, molecule in molecules:
atoms, molecule_y = build_atom_pairs(name, molecule)
amolecule = np.zeros((650, atoms.shape[1], atoms.shape[2]))
amolecule[:atoms.shape[0], :atoms.shape[1], :atoms.shape[2]] = atoms
amolecule = amolecule.transpose([0, 2, 1]).reshape(amolecule.shape[0], -1)
amolecule_y = np.zeros((650, molecule_y.shape[1]))
amolecule_y[:molecule_y.shape[0], :molecule_y.shape[1]] = molecule_y
moleculelist.append(amolecule)
molecule_ylist.append(amolecule_y)
c = c + 1
if c > 10000:
break
def BRNNModel(inputdim):
model = Sequential()
model.add(Bidirectional(LSTM(100, return_sequences=True, input_dim=inputdim)))
model.add(Dense(8))
model.add(Activation('relu'))
return model
def batch_generator(X, y, batch_size):
number_of_batches = X.shape[0] / batch_size
counter = 0
shuffle_index = np.arange(np.shape(y)[0])
while 1:
index_batch = shuffle_index[batch_size * counter:batch_size * (counter + 1)]
X_batch = X[index_batch, :].todense()
y_batch = y[index_batch]
counter += 1
yield (np.array(X_batch), y_batch)
if counter > number_of_batches:
counter = 0
X = np.asarray(moleculelist)
y = np.asarray(molecule_ylist)
model = BRNNModel(X.shape[1])
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy']) | code |
17136911/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Bidirectional
from keras.layers import Input, Dense, Activation, BatchNormalization, Flatten
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
le = LabelEncoder()
oh = OneHotEncoder(sparse=False)
df = pd.read_csv('../input/train.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index_0', 'atom_index_1', 'type', 'scalar_coupling_constant'])
df_m_coupling_contributions = pd.read_csv('../input/scalar_coupling_contributions.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index_0', 'atom_index_1', 'fc', 'sd', 'pso', 'dso'])
df = pd.merge(df, df_m_coupling_contributions, on=['molecule_name', 'atom_index_0', 'atom_index_1'])
df_m_diple_moments = pd.read_csv('../input/dipole_moments.csv', sep=',', header=0, usecols=['molecule_name', 'X', 'Y', 'Z'])
df = pd.merge(df, df_m_diple_moments, on='molecule_name')
df_m_pot_engy = pd.read_csv('../input/potential_energy.csv', sep=',', header=0, usecols=['molecule_name', 'potential_energy'])
df = pd.merge(df, df_m_pot_engy, on='molecule_name')
df_a_str = pd.read_csv('../input/structures.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'atom', 'x', 'y', 'z'])
f = df_a_str['atom'].values
f = np.reshape(f, (-1, 1))
f = oh.fit_transform(f)
ohdf = pd.DataFrame(f)
df_a_str = pd.concat([df_a_str, ohdf], axis=1)
df_a_str = df_a_str.rename(index=str, columns={0: 'A0', 1: 'A1', 2: 'A2', 3: 'A3', 4: 'A4'})
df_a_str.drop(columns=['atom'], inplace=True)
df_a_mag_sh_tensor = pd.read_csv('../input/magnetic_shielding_tensors.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'XX', 'YX', 'ZX', 'XY', 'YY', 'ZY', 'XZ', 'YZ', 'ZZ'])
df_a_mlkn_charges = pd.read_csv('../input/mulliken_charges.csv', sep=',', header=0, usecols=['molecule_name', 'atom_index', 'mulliken_charge'])
df_a_str = pd.merge(df_a_str, df_a_mag_sh_tensor, on=['molecule_name', 'atom_index'])
df_a_str = pd.merge(df_a_str, df_a_mlkn_charges, on=['molecule_name', 'atom_index'])
df_atom_1_prop = df_a_str.rename(index=str, columns={'atom_index': 'atom_index_0', 'A0': 'A0_0', 'A1': 'A1_0', 'A2': 'A2_0', 'A3': 'A3_0', 'A4': 'A4_0', 'x': 'x_0', 'y': 'y_0', 'z': 'z_0', 'XX': 'XX_0', 'YX': 'YX_0', 'ZX': 'ZX_0', 'XY': 'XY_0', 'YY': 'YY_0', 'ZY': 'ZY_0', 'XZ': 'XZ_0', 'YZ': 'YZ_0', 'ZZ': 'ZZ_0', 'mulliken_charge': 'mulliken_charge_0'})
df = pd.merge(df, df_atom_1_prop, on=['molecule_name', 'atom_index_0'])
df_atom_2_prop = df_a_str.rename(index=str, columns={'atom_index': 'atom_index_1', 'A0': 'A0_1', 'A1': 'A1_1', 'A2': 'A2_1', 'A3': 'A3_1', 'A4': 'A4_1', 'x': 'x_1', 'y': 'y_1', 'z': 'z_1', 'XX': 'XX_1', 'YX': 'YX_1', 'ZX': 'ZX_1', 'XY': 'XY_1', 'YY': 'YY_1', 'ZY': 'ZY_1', 'XZ': 'XZ_1', 'YZ': 'YZ_1', 'ZZ': 'ZZ_1', 'mulliken_charge': 'mulliken_charge_1'})
df = pd.merge(df, df_atom_2_prop, on=['molecule_name', 'atom_index_1'])
ss = StandardScaler()
df[['scalar_coupling_constant', 'fc', 'sd', 'pso', 'dso', 'X', 'Y', 'Z', 'potential_energy', 'x_0', 'y_0', 'z_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'x_1', 'y_1', 'z_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1']] = ss.fit_transform(df[['scalar_coupling_constant', 'fc', 'sd', 'pso', 'dso', 'X', 'Y', 'Z', 'potential_energy', 'x_0', 'y_0', 'z_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'x_1', 'y_1', 'z_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1']])
def build_atom_pairs(name, molecule):
df = molecule.apply(list)
atom_pair_y = np.zeros((df.shape[0], 8))
atom_pair = np.zeros((df.shape[0], 2, 18))
atom_pair[:, 0, :] = df.as_matrix(columns=['x_0', 'y_0', 'z_0', 'XX_0', 'YX_0', 'ZX_0', 'XY_0', 'YY_0', 'ZY_0', 'XZ_0', 'YZ_0', 'ZZ_0', 'mulliken_charge_0', 'A0_0', 'A1_0', 'A2_0', 'A3_0', 'A4_0'])
atom_pair[:, 1, :] = df.as_matrix(columns=['x_1', 'y_1', 'z_1', 'XX_1', 'YX_1', 'ZX_1', 'XY_1', 'YY_1', 'ZY_1', 'XZ_1', 'YZ_1', 'ZZ_1', 'mulliken_charge_1', 'A0_1', 'A1_1', 'A2_1', 'A3_1', 'A4_1'])
atom_pair_y = df.as_matrix(columns=['potential_energy', 'X', 'Y', 'Z', 'fc', 'sd', 'pso', 'dso'])
return (atom_pair, atom_pair_y)
moleculelist = []
molecule_ylist = []
molecules = df.groupby('molecule_name')
c = 0
for name, molecule in molecules:
atoms, molecule_y = build_atom_pairs(name, molecule)
amolecule = np.zeros((650, atoms.shape[1], atoms.shape[2]))
amolecule[:atoms.shape[0], :atoms.shape[1], :atoms.shape[2]] = atoms
amolecule = amolecule.transpose([0, 2, 1]).reshape(amolecule.shape[0], -1)
amolecule_y = np.zeros((650, molecule_y.shape[1]))
amolecule_y[:molecule_y.shape[0], :molecule_y.shape[1]] = molecule_y
moleculelist.append(amolecule)
molecule_ylist.append(amolecule_y)
c = c + 1
if c > 10000:
break
def BRNNModel(inputdim):
model = Sequential()
model.add(Bidirectional(LSTM(100, return_sequences=True, input_dim=inputdim)))
model.add(Dense(8))
model.add(Activation('relu'))
return model
def batch_generator(X, y, batch_size):
number_of_batches = X.shape[0] / batch_size
counter = 0
shuffle_index = np.arange(np.shape(y)[0])
while 1:
index_batch = shuffle_index[batch_size * counter:batch_size * (counter + 1)]
X_batch = X[index_batch, :].todense()
y_batch = y[index_batch]
counter += 1
yield (np.array(X_batch), y_batch)
if counter > number_of_batches:
counter = 0
X = np.asarray(moleculelist)
y = np.asarray(molecule_ylist)
model = BRNNModel(X.shape[1])
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
model.fit(X_train, Y_train, epochs=30, batch_size=16, verbose=2) | code |
16118732/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from PIL import Image
import cv2
import glob
import glob
import os
import os
import os
import os
import pydicom
import pydicom
import pydicom
import numpy as np
import pandas as pd
import os
import cv2
import os
import pydicom
inputdir = '../input/sample images/'
outdir = './'
test_list = [os.path.basename(x) for x in glob.glob(inputdir + './*.dcm')]
for f in test_list:
ds = pydicom.read_file(inputdir + f)
img = ds.pixel_array
cv2.imwrite(outdir + f.replace('.dcm', '.png'), img)
import os
import pydicom
import glob
from PIL import Image
inputdir = '../input/sample images/'
outdir = './'
test_list = [os.path.basename(x) for x in glob.glob(inputdir + './*.dcm')]
for f in test_list:
ds = pydicom.read_file(inputdir + f)
img = ds.pixel_array
img_mem = Image.fromarray(img)
img_mem.save(outdir + f.replace('.dcm', '.png'))
import os
import pydicom
import glob
from PIL import Image
inputdir = '../input/sample images/'
outdir = './'
test_list = [os.path.basename(x) for x in glob.glob(inputdir + './*.dcm')]
for f in test_list:
ds = pydicom.read_file(inputdir + f)
img = ds.pixel_array
img_mem = Image.fromarray(img)
img_mem.save(outdir + f.replace('.dcm', '.jp2')) | code |
16118732/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
import os
import os
import pydicom
import numpy as np
import pandas as pd
import os
import cv2
import os
import pydicom
inputdir = '../input/sample images/'
outdir = './'
test_list = [os.path.basename(x) for x in glob.glob(inputdir + './*.dcm')]
for f in test_list:
ds = pydicom.read_file(inputdir + f)
img = ds.pixel_array
cv2.imwrite(outdir + f.replace('.dcm', '.png'), img) | code |
16118732/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input/sample images')) | code |
323056/cell_4 | [
"text_plain_output_1.png"
] | imgs.keys()
(imgs[1].shape, masks[1].shape) | code |
323056/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
import glob
import numpy as np
def load_cv2_images(folder):
imgs, masks, img_ids = ({}, {}, {})
for i in range(47):
imgs[i + 1] = []
masks[i + 1] = []
img_ids[i + 1] = []
paths = glob.glob(folder + '*.tif')
paths = [p for p in paths if 'mask' not in p]
for p in paths:
index = int(p.split('/')[3].split('_')[0])
try:
imgs[index].append(cv2.imread(p, 0))
masks[index].append(cv2.imread(p[:-4] + '_mask.tif', 0))
img_ids[index].append(p.split('/')[3])
except:
pass
for i in range(47):
imgs[i + 1] = np.array(imgs[i + 1])
masks[i + 1] = np.array(masks[i + 1])
return (imgs, masks, img_ids)
imgs, masks, img_ids = load_cv2_images('../input/train/')
imgs.keys()
def find_pairs(compare_img, compare_mask, compare_id, imgs, masks, img_ids, compare_index, matches):
threshold = 23000000
for i, (img, mask, img_id) in enumerate(zip(imgs, masks, img_ids)):
if np.abs(compare_img - img).sum() < threshold and i != compare_index and ((compare_mask.sum() == 0) != (mask.sum() == 0)):
matches.append((compare_img, compare_mask, compare_id, img, mask, img_id))
return matches
matches = []
for j in range(47):
for i, (img, mask, img_id) in enumerate(zip(imgs[j + 1], masks[j + 1], img_ids[j + 1])):
matches = find_pairs(img, mask, img_id, imgs[j + 1], masks[j + 1], img_ids[j + 1], i, matches)
len(matches)
repeats, unique = ([], [])
for i, m in enumerate(matches):
if m[0].sum() not in repeats or m[3].sum() not in repeats:
unique.append(m[0].sum())
fig, ax = plt.subplots(2, 2)
if m[1].sum() == 0:
i1, i2 = (1, 0)
else:
i1, i2 = (0, 1)
ax[i1][0].imshow(m[0], cmap='hot')
ax[i1][0].set_title(m[2])
ax[i1][1].imshow(m[1], cmap='hot')
ax[i1][1].set_title(m[2][:-4] + '_mask.tif')
ax[i2][0].imshow(m[3], cmap='hot')
ax[i2][0].set_title(m[5])
ax[i2][1].imshow(m[4], cmap='hot')
ax[i2][1].set_title(m[5][:-4] + '_mask.tif')
fig.subplots_adjust(hspace=0.4)
plt.show()
repeats.append(m[0].sum())
repeats.append(m[3].sum()) | code |
323056/cell_7 | [
"text_plain_output_1.png"
] | import cv2
import glob
import numpy as np
def load_cv2_images(folder):
imgs, masks, img_ids = ({}, {}, {})
for i in range(47):
imgs[i + 1] = []
masks[i + 1] = []
img_ids[i + 1] = []
paths = glob.glob(folder + '*.tif')
paths = [p for p in paths if 'mask' not in p]
for p in paths:
index = int(p.split('/')[3].split('_')[0])
try:
imgs[index].append(cv2.imread(p, 0))
masks[index].append(cv2.imread(p[:-4] + '_mask.tif', 0))
img_ids[index].append(p.split('/')[3])
except:
pass
for i in range(47):
imgs[i + 1] = np.array(imgs[i + 1])
masks[i + 1] = np.array(masks[i + 1])
return (imgs, masks, img_ids)
imgs, masks, img_ids = load_cv2_images('../input/train/')
imgs.keys()
def find_pairs(compare_img, compare_mask, compare_id, imgs, masks, img_ids, compare_index, matches):
threshold = 23000000
for i, (img, mask, img_id) in enumerate(zip(imgs, masks, img_ids)):
if np.abs(compare_img - img).sum() < threshold and i != compare_index and ((compare_mask.sum() == 0) != (mask.sum() == 0)):
matches.append((compare_img, compare_mask, compare_id, img, mask, img_id))
return matches
matches = []
for j in range(47):
for i, (img, mask, img_id) in enumerate(zip(imgs[j + 1], masks[j + 1], img_ids[j + 1])):
matches = find_pairs(img, mask, img_id, imgs[j + 1], masks[j + 1], img_ids[j + 1], i, matches)
len(matches)
# Print the matches, avoiding duplicates
repeats, unique = [], []
for i, m in enumerate(matches):
# Using pixel sums as an ID for the picture
if m[0].sum() not in repeats\
or m[3].sum() not in repeats:
unique.append(m[0].sum())
fig, ax = plt.subplots(2, 2)
if m[1].sum() == 0:
i1, i2 = 1, 0
else:
i1, i2 = 0, 1
ax[i1][0].imshow(m[0], cmap='hot')
ax[i1][0].set_title(m[2])
ax[i1][1].imshow(m[1], cmap='hot')
ax[i1][1].set_title(m[2][:-4]+'_mask.tif')
ax[i2][0].imshow(m[3], cmap='hot')
ax[i2][0].set_title(m[5])
ax[i2][1].imshow(m[4], cmap='hot')
ax[i2][1].set_title(m[5][:-4]+'_mask.tif')
fig.subplots_adjust(hspace=0.4)
plt.show()
repeats.append(m[0].sum())
repeats.append(m[3].sum())
len(unique) | code |
323056/cell_3 | [
"text_plain_output_1.png"
] | imgs.keys() | code |
323056/cell_5 | [
"text_plain_output_1.png"
] | import cv2
import glob
import numpy as np
def load_cv2_images(folder):
imgs, masks, img_ids = ({}, {}, {})
for i in range(47):
imgs[i + 1] = []
masks[i + 1] = []
img_ids[i + 1] = []
paths = glob.glob(folder + '*.tif')
paths = [p for p in paths if 'mask' not in p]
for p in paths:
index = int(p.split('/')[3].split('_')[0])
try:
imgs[index].append(cv2.imread(p, 0))
masks[index].append(cv2.imread(p[:-4] + '_mask.tif', 0))
img_ids[index].append(p.split('/')[3])
except:
pass
for i in range(47):
imgs[i + 1] = np.array(imgs[i + 1])
masks[i + 1] = np.array(masks[i + 1])
return (imgs, masks, img_ids)
imgs, masks, img_ids = load_cv2_images('../input/train/')
imgs.keys()
def find_pairs(compare_img, compare_mask, compare_id, imgs, masks, img_ids, compare_index, matches):
threshold = 23000000
for i, (img, mask, img_id) in enumerate(zip(imgs, masks, img_ids)):
if np.abs(compare_img - img).sum() < threshold and i != compare_index and ((compare_mask.sum() == 0) != (mask.sum() == 0)):
matches.append((compare_img, compare_mask, compare_id, img, mask, img_id))
return matches
matches = []
for j in range(47):
for i, (img, mask, img_id) in enumerate(zip(imgs[j + 1], masks[j + 1], img_ids[j + 1])):
matches = find_pairs(img, mask, img_id, imgs[j + 1], masks[j + 1], img_ids[j + 1], i, matches)
len(matches) | code |
72071704/cell_13 | [
"text_plain_output_1.png"
] | 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 plotly.express as px
import seaborn as sns
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df1 = df.groupby('Country')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
df1 = df.groupby('City')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
df1 = df.groupby('State')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
df1 = df.groupby('AirportName')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
df['lon'] = df.Centroid.apply(lambda x: x.split(' ')[0].replace('POINT(', ' '))
df['lat'] = df.Centroid.apply(lambda x: x.split(' ')[1].replace(')', ' '))
df1 = df.groupby(['Country', 'City', 'lat', 'lon'])['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
fig = px.scatter_geo(df1, lat='lat', lon='lon', hover_name='Country', color='Country', hover_data=['PercentOfBaseline', 'City'], labels={'PercentOfBaseline': 'Percent of Baseline'})
fig.update_geos(showocean=True, oceancolor='LightCyan', lakecolor='LightSteelBlue', showlakes=True)
fig.show() | code |
72071704/cell_9 | [
"text_html_output_2.png"
] | 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 sns
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df1 = df.groupby('Country')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
df1 = df.groupby('City')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
plt.figure(figsize=[10, 10])
sns.barplot(data=df1, x='PercentOfBaseline', y='City', palette='GnBu')
plt.xlabel('Percent of baseline') | code |
72071704/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.head() | code |
72071704/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum() | code |
72071704/cell_11 | [
"text_plain_output_1.png"
] | 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 sns
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df1 = df.groupby('Country')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
df1 = df.groupby('City')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
df1 = df.groupby('State')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
df1 = df.groupby('AirportName')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
plt.figure(figsize=[10, 10])
sns.barplot(data=df1, x='PercentOfBaseline', y='AirportName', palette='crest')
plt.xlabel('Percent of baseline')
plt.ylabel('Airport name') | code |
72071704/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 |
72071704/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df['Country'].unique() | code |
72071704/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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 sns
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df1 = df.groupby('Country')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
plt.figure(figsize=[10, 7])
sns.barplot(data=df1, x='Country', y='PercentOfBaseline', palette='GnBu_r')
plt.ylabel('Percent of baseline') | code |
72071704/cell_10 | [
"text_html_output_1.png"
] | 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 sns
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df1 = df.groupby('Country')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
df1 = df.groupby('City')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
df1 = df.groupby('State')['PercentOfBaseline'].mean().sort_values(ascending=False).reset_index()
sns.set(font_scale=1.2)
plt.figure(figsize=[10, 10])
sns.barplot(data=df1, x='PercentOfBaseline', y='State', palette='Greens')
plt.xlabel('Percent of baseline') | code |
72071704/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.info() | code |
1004678/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
houseprice_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
houseprice_df.head() | code |
1004678/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1004678/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
houseprice_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
houseprice_df.info()
print('----------------------------')
test_df.info() | code |
1004678/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
houseprice_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
houseprice_df = houseprice_df.drop(['Alley', 'PoolQC', 'Fence'], axis=1)
test_df = test_df.drop(['Alley', 'PoolQC', 'Fence'], axis=1)
print(pd.value_counts(houseprice_df['MSSubClass'].values, sort=False))
houseprice_df['MSSubClass'].plot(kind='hist', figsize=(15, 3), bins=50, xlim=(0, 100))
sns.factorplot('MSSubClass', 'Survived', order=[1, 2, 3], data=titanic_df, size=5) | code |
89138107/cell_21 | [
"text_plain_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(51, 76)]
high_correlated_cols(train_df[cols], plot=True)
drop_list3 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(76, 101)]
high_correlated_cols(train_df[cols], plot=True)
drop_list4 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(101, 126)]
high_correlated_cols(train_df[cols], plot=True)
drop_list5 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(126, 151)]
high_correlated_cols(train_df[cols], plot=True)
drop_list6 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(151, 176)]
high_correlated_cols(train_df[cols], plot=True)
drop_list7 = high_correlated_cols(train_df[cols], plot=False) | code |
89138107/cell_13 | [
"text_plain_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(51, 76)]
high_correlated_cols(train_df[cols], plot=True)
drop_list3 = high_correlated_cols(train_df[cols], plot=False) | code |
89138107/cell_9 | [
"text_plain_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False) | code |
89138107/cell_23 | [
"text_plain_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(51, 76)]
high_correlated_cols(train_df[cols], plot=True)
drop_list3 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(76, 101)]
high_correlated_cols(train_df[cols], plot=True)
drop_list4 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(101, 126)]
high_correlated_cols(train_df[cols], plot=True)
drop_list5 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(126, 151)]
high_correlated_cols(train_df[cols], plot=True)
drop_list6 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(151, 176)]
high_correlated_cols(train_df[cols], plot=True)
drop_list7 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(176, 201)]
high_correlated_cols(train_df[cols], plot=True)
drop_list8 = high_correlated_cols(train_df[cols], plot=False) | code |
89138107/cell_20 | [
"image_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(51, 76)]
high_correlated_cols(train_df[cols], plot=True)
drop_list3 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(76, 101)]
high_correlated_cols(train_df[cols], plot=True)
drop_list4 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(101, 126)]
high_correlated_cols(train_df[cols], plot=True)
drop_list5 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(126, 151)]
high_correlated_cols(train_df[cols], plot=True)
drop_list6 = high_correlated_cols(train_df[cols], plot=False)
drop_list6 | code |
89138107/cell_11 | [
"text_plain_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False) | code |
89138107/cell_19 | [
"text_plain_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(51, 76)]
high_correlated_cols(train_df[cols], plot=True)
drop_list3 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(76, 101)]
high_correlated_cols(train_df[cols], plot=True)
drop_list4 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(101, 126)]
high_correlated_cols(train_df[cols], plot=True)
drop_list5 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(126, 151)]
high_correlated_cols(train_df[cols], plot=True)
drop_list6 = high_correlated_cols(train_df[cols], plot=False) | code |
89138107/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 |
89138107/cell_18 | [
"image_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(51, 76)]
high_correlated_cols(train_df[cols], plot=True)
drop_list3 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(76, 101)]
high_correlated_cols(train_df[cols], plot=True)
drop_list4 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(101, 126)]
high_correlated_cols(train_df[cols], plot=True)
drop_list5 = high_correlated_cols(train_df[cols], plot=False)
drop_list5 | code |
89138107/cell_15 | [
"text_plain_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(51, 76)]
high_correlated_cols(train_df[cols], plot=True)
drop_list3 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(76, 101)]
high_correlated_cols(train_df[cols], plot=True)
drop_list4 = high_correlated_cols(train_df[cols], plot=False) | code |
89138107/cell_16 | [
"image_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(51, 76)]
high_correlated_cols(train_df[cols], plot=True)
drop_list3 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(76, 101)]
high_correlated_cols(train_df[cols], plot=True)
drop_list4 = high_correlated_cols(train_df[cols], plot=False)
drop_list4 | code |
89138107/cell_17 | [
"text_plain_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(51, 76)]
high_correlated_cols(train_df[cols], plot=True)
drop_list3 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(76, 101)]
high_correlated_cols(train_df[cols], plot=True)
drop_list4 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(101, 126)]
high_correlated_cols(train_df[cols], plot=True)
drop_list5 = high_correlated_cols(train_df[cols], plot=False) | code |
89138107/cell_14 | [
"image_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(51, 76)]
high_correlated_cols(train_df[cols], plot=True)
drop_list3 = high_correlated_cols(train_df[cols], plot=False)
drop_list3 | code |
89138107/cell_22 | [
"image_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(51, 76)]
high_correlated_cols(train_df[cols], plot=True)
drop_list3 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(76, 101)]
high_correlated_cols(train_df[cols], plot=True)
drop_list4 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(101, 126)]
high_correlated_cols(train_df[cols], plot=True)
drop_list5 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(126, 151)]
high_correlated_cols(train_df[cols], plot=True)
drop_list6 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(151, 176)]
high_correlated_cols(train_df[cols], plot=True)
drop_list7 = high_correlated_cols(train_df[cols], plot=False)
drop_list7 | code |
89138107/cell_10 | [
"image_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
drop_list1 | code |
89138107/cell_12 | [
"image_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)
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd.read_csv('../input/ieee-fraud-detection/train_transaction.csv')
train_identity = pd.read_csv('../input/ieee-fraud-detection/train_identity.csv')
test_transaction = pd.read_csv('../input/ieee-fraud-detection/test_transaction.csv')
test_identity = pd.read_csv('../input/ieee-fraud-detection/test_identity.csv')
sample_submission = pd.read_csv('../input/ieee-fraud-detection/sample_submission.csv')
train_df = train_transaction.merge(train_identity, how='left', on='TransactionID')
test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
def high_correlated_cols(dataframe, plot=True, corr_th=0.85):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
sns.set(rc={'figure.figsize': (25, 25)})
return drop_list
cols = ['V' + str(x) for x in range(1, 26)]
high_correlated_cols(train_df[cols], plot=True)
drop_list1 = high_correlated_cols(train_df[cols], plot=False)
cols = ['V' + str(x) for x in range(26, 51)]
high_correlated_cols(train_df[cols], plot=True)
drop_list2 = high_correlated_cols(train_df[cols], plot=False)
drop_list2 | code |
32073488/cell_21 | [
"text_plain_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
maxlen = 130
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)
for i in x_train[0:10]:
print(len(i)) | code |
32073488/cell_13 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
print(type(word_index))
print(len(word_index)) | code |
32073488/cell_9 | [
"image_output_1.png"
] | d = x_train[0]
print(len(d)) | code |
32073488/cell_25 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import SimpleRNN, Dense, Activation
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
num_words = 15000
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=num_words)
maxlen = 130
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)
rnn = Sequential()
rnn.add(Embedding(num_words, 32, input_length=len(x_train[0])))
rnn.add(SimpleRNN(16, input_shape=(num_words, maxlen), return_sequences=False, activation='relu'))
rnn.add(Dense(1))
rnn.add(Activation('sigmoid'))
rnn.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
history = rnn.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=6, batch_size=128, verbose=1)
score = rnn.evaluate(x_test, y_test)
print('Accuracy: %', score[1] * 100) | code |
32073488/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
unique, counts = np.unique(y_train, return_counts=True)
print('Y Train distrubution:', dict(zip(unique, counts))) | code |
32073488/cell_23 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import SimpleRNN, Dense, Activation
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
num_words = 15000
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=num_words)
maxlen = 130
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)
rnn = Sequential()
rnn.add(Embedding(num_words, 32, input_length=len(x_train[0])))
rnn.add(SimpleRNN(16, input_shape=(num_words, maxlen), return_sequences=False, activation='relu'))
rnn.add(Dense(1))
rnn.add(Activation('sigmoid'))
print(rnn.summary())
rnn.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) | code |
32073488/cell_20 | [
"image_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
maxlen = 130
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)
print(x_train[5]) | code |
32073488/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
plt.figure()
sns.countplot(y_train)
plt.xlabel('Classes')
plt.ylabel('Freq')
plt.title('y train')
plt.show() | code |
32073488/cell_26 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import SimpleRNN, Dense, Activation
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
import matplotlib.pyplot as plt
import seaborn as sns
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
review_len_train = []
review_len_test = []
for i, ii in zip(x_train, x_test):
review_len_train.append(len(i))
review_len_test.append(len(ii))
word_index = imdb.get_word_index()
num_words = 15000
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=num_words)
maxlen = 130
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)
rnn = Sequential()
rnn.add(Embedding(num_words, 32, input_length=len(x_train[0])))
rnn.add(SimpleRNN(16, input_shape=(num_words, maxlen), return_sequences=False, activation='relu'))
rnn.add(Dense(1))
rnn.add(Activation('sigmoid'))
rnn.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
history = rnn.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=6, batch_size=128, verbose=1)
plt.figure()
plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='test')
plt.title('accuracy')
plt.ylabel('accuracy')
plt.xlabel('epochs')
plt.legend()
plt.show() | code |
32073488/cell_2 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3) | code |
32073488/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
review_len_train = []
review_len_test = []
for i, ii in zip(x_train, x_test):
review_len_train.append(len(i))
review_len_test.append(len(ii))
sns.distplot(review_len_train, hist_kws={'alpha': 0.3})
sns.distplot(review_len_test, hist_kws={'alpha': 0.3})
plt.show() | code |
32073488/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers.embeddings import Embedding
from keras.layers import SimpleRNN, Dense, Activation | code |
32073488/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
plt.figure()
sns.countplot(y_test)
plt.xlabel('Classes')
plt.ylabel('Freq')
plt.title('y test')
plt.show() | code |
32073488/cell_8 | [
"image_output_1.png"
] | d = x_train[0]
print(x_train[0]) | code |
32073488/cell_15 | [
"image_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
for keys, values in word_index.items():
if values == 4:
print(keys) | code |
32073488/cell_16 | [
"image_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
for keys, values in word_index.items():
if values == 123:
print(keys) | code |
32073488/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
print('Y Train Values:', np.unique(y_train))
print('Y Test Values:', np.unique(y_test)) | code |
32073488/cell_17 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
def whatItSay(index=9):
reverse_index = dict([(value, key) for key, value in word_index.items()])
decode_review = ' '.join([reverse_index.get(i - 3, '!') for i in x_train[index]])
print(decode_review)
print(y_train[index])
return decode_review
decoded_review = whatItSay() | code |
32073488/cell_24 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import SimpleRNN, Dense, Activation
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
num_words = 15000
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=num_words)
maxlen = 130
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)
rnn = Sequential()
rnn.add(Embedding(num_words, 32, input_length=len(x_train[0])))
rnn.add(SimpleRNN(16, input_shape=(num_words, maxlen), return_sequences=False, activation='relu'))
rnn.add(Dense(1))
rnn.add(Activation('sigmoid'))
rnn.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
history = rnn.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=6, batch_size=128, verbose=1) | code |
32073488/cell_14 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
for keys, values in word_index.items():
if values == 1:
print(keys) | code |
32073488/cell_22 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
def whatItSay(index=9):
reverse_index = dict([(value, key) for key, value in word_index.items()])
decode_review = ' '.join([reverse_index.get(i - 3, '!') for i in x_train[index]])
return decode_review
decoded_review = whatItSay()
decoded_review = whatItSay(5) | code |
32073488/cell_27 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import SimpleRNN, Dense, Activation
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
import matplotlib.pyplot as plt
import seaborn as sns
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
review_len_train = []
review_len_test = []
for i, ii in zip(x_train, x_test):
review_len_train.append(len(i))
review_len_test.append(len(ii))
word_index = imdb.get_word_index()
num_words = 15000
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=num_words)
maxlen = 130
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)
rnn = Sequential()
rnn.add(Embedding(num_words, 32, input_length=len(x_train[0])))
rnn.add(SimpleRNN(16, input_shape=(num_words, maxlen), return_sequences=False, activation='relu'))
rnn.add(Dense(1))
rnn.add(Activation('sigmoid'))
rnn.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
history = rnn.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=6, batch_size=128, verbose=1)
plt.figure()
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='test')
plt.title('acc')
plt.ylabel('Acc')
plt.xlabel('epochs')
plt.legend()
plt.show() | code |
32073488/cell_12 | [
"text_plain_output_1.png"
] | from scipy import stats
import numpy as np
unique, counts = np.unique(y_train, return_counts=True)
unique, counts = np.unique(y_test, return_counts=True)
review_len_train = []
review_len_test = []
for i, ii in zip(x_train, x_test):
review_len_train.append(len(i))
review_len_test.append(len(ii))
print('Train mean:', np.mean(review_len_train))
print('Train median:', np.median(review_len_train))
print('Train mode:', stats.mode(review_len_train)) | code |
32073488/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
unique, counts = np.unique(y_train, return_counts=True)
unique, counts = np.unique(y_test, return_counts=True)
print('Y Test distrubution:', dict(zip(unique, counts))) | code |
73071444/cell_42 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data = train_data.drop(['Cabin', 'Ticket', 'PassengerId'], axis=1)
test_data = test_data.drop(['Cabin', 'Ticket'], axis=1)
combine = [train_data, test_data]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
(pd.crosstab(train_data['Title'], train_data['Sex']), pd.crosstab(test_data['Title'], test_data['Sex']))
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
pd.crosstab(train_data['Title'], train_data['Sex'])
train_data = train_data.drop(['Name'], axis=1)
test_data = test_data.drop(['Name'], axis=1)
combine = [train_data, test_data]
train_data = pd.concat([train_data, pd.get_dummies(train_data['Sex'])], axis=1)
test_data = pd.concat([test_data, pd.get_dummies(test_data['Sex'])], axis=1)
train_data | code |
73071444/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data[['Pclass', 'Survived']].groupby('Pclass', as_index=False).mean() | code |
73071444/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.describe(include=['O']) | code |
73071444/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
age_plt = sns.FacetGrid(train_data, col='Survived')
age_plt.map(plt.hist, 'Age', bins=20)
class_age_plt = sns.FacetGrid(train_data, col='Survived', row='Pclass', height=2.8, aspect=2.0)
class_age_plt.map(plt.hist, 'Age', bins=20)
cat = sns.FacetGrid(train_data, row='Embarked', height=2.2, aspect=1.6)
cat.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')
fare_plt = sns.FacetGrid(train_data, col='Survived')
fare_plt.map(plt.hist, 'Fare')
fare_embarked_plt = sns.FacetGrid(train_data, col='Survived', row='Embarked')
fare_embarked_plt.map(sns.barplot, 'Sex', 'Fare', ci=None) | code |
73071444/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.head() | code |
73071444/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data = train_data.drop(['Cabin', 'Ticket', 'PassengerId'], axis=1)
test_data = test_data.drop(['Cabin', 'Ticket'], axis=1)
combine = [train_data, test_data]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
(pd.crosstab(train_data['Title'], train_data['Sex']), pd.crosstab(test_data['Title'], test_data['Sex'])) | code |
73071444/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
age_plt = sns.FacetGrid(train_data, col='Survived')
age_plt.map(plt.hist, 'Age', bins=20)
class_age_plt = sns.FacetGrid(train_data, col='Survived', row='Pclass', height=2.8, aspect=2.0)
class_age_plt.map(plt.hist, 'Age', bins=20) | code |
73071444/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
test_data.info() | code |
73071444/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data = train_data.drop(['Cabin', 'Ticket', 'PassengerId'], axis=1)
test_data = test_data.drop(['Cabin', 'Ticket'], axis=1)
train_data = train_data.drop(['Name'], axis=1)
test_data = test_data.drop(['Name'], axis=1)
combine = [train_data, test_data]
train_data.head() | code |
73071444/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
age_plt = sns.FacetGrid(train_data, col='Survived')
age_plt.map(plt.hist, 'Age', bins=20) | code |
73071444/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
(train_data['Ticket'].unique().shape, test_data['Ticket'].unique().shape) | code |
73071444/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.describe() | code |
73071444/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data[['SibSp', 'Survived']].groupby('SibSp', as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
73071444/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data[['Parch', 'Survived']].groupby('Parch', as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
73071444/cell_38 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data = train_data.drop(['Cabin', 'Ticket', 'PassengerId'], axis=1)
test_data = test_data.drop(['Cabin', 'Ticket'], axis=1)
train_data[['Title', 'Survived']].groupby('Title', as_index=False).mean() | code |
73071444/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data = train_data.drop(['Cabin', 'Ticket', 'PassengerId'], axis=1)
test_data = test_data.drop(['Cabin', 'Ticket'], axis=1)
combine = [train_data, test_data]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
(pd.crosstab(train_data['Title'], train_data['Sex']), pd.crosstab(test_data['Title'], test_data['Sex']))
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
pd.crosstab(train_data['Title'], train_data['Sex'])
train_data = train_data.drop(['Name'], axis=1)
test_data = test_data.drop(['Name'], axis=1)
combine = [train_data, test_data]
train_data = pd.concat([train_data, pd.get_dummies(train_data['Sex'])], axis=1)
test_data = pd.concat([test_data, pd.get_dummies(test_data['Sex'])], axis=1)
train_data
train_data.drop(['female'], axis=1, inplace=True)
test_data.drop(['female'], axis=1, inplace=True)
train_data = pd.concat([train_data, pd.get_dummies(train_data['Title'])], axis=1)
test_data = pd.concat([test_data, pd.get_dummies(test_data['Title'])], axis=1)
combine = [train_data, test_data]
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_data.head() | code |
73071444/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
age_plt = sns.FacetGrid(train_data, col='Survived')
age_plt.map(plt.hist, 'Age', bins=20)
class_age_plt = sns.FacetGrid(train_data, col='Survived', row='Pclass', height=2.8, aspect=2.0)
class_age_plt.map(plt.hist, 'Age', bins=20)
cat = sns.FacetGrid(train_data, row='Embarked', height=2.2, aspect=1.6)
cat.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')
fare_plt = sns.FacetGrid(train_data, col='Survived')
fare_plt.map(plt.hist, 'Fare') | code |
73071444/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data[['Embarked', 'Survived']].groupby('Embarked', as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
73071444/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
age_plt = sns.FacetGrid(train_data, col='Survived')
age_plt.map(plt.hist, 'Age', bins=20)
class_age_plt = sns.FacetGrid(train_data, col='Survived', row='Pclass', height=2.8, aspect=2.0)
class_age_plt.map(plt.hist, 'Age', bins=20)
cat = sns.FacetGrid(train_data, row='Embarked', height=2.2, aspect=1.6)
cat.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep') | code |
73071444/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean() | code |
73071444/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.info() | code |
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