path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
17123567/cell_10 | [
"text_plain_output_1.png"
] | from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('hacker_news', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref = dataset_ref.table('full')
table = client.get_table(table_ref)
table.schema
client.list_rows(table, max_results=5).to_dataframe() | code |
130010025/cell_11 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import metrics
from sklearn import model_selection
from sklearn import preprocessing
import numpy as np
import os
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
class CFG:
HOME_DIR = '/kaggle/input/icr-identify-age-related-conditions'
SPLITS = 5
SEED = 2023
boosting_type = 'gbdt'
ITERATION = 1000
BOOSTING_TYPE = 'dart'
lgb_params = {'objective': 'binary', 'metric': None, 'boosting': BOOSTING_TYPE, 'learning_rate': 0.005, 'num_leaves': 5, 'feature_fraction': 0.5, 'bagging_fraction': 0.8, 'lambda_l1': 2, 'lambda_l2': 4, 'n_jobs': -1, 'seed': SEED}
xgb_params = {'objective': 'binary:logistic', 'eval_metric': 'logloss', 'learning_rate': 0.005, 'max_depth': 4, 'colsample_bytree': 0.5, 'subsample': 0.8, 'eta': 0.03, 'gamma': 1.5, 'random_state': SEED}
cat_params = {'learning_rate': 0.005, 'iterations': ITERATION, 'depth': 4, 'colsample_bylevel': 0.5, 'subsample': 0.8, 'l2_leaf_reg': 3, 'random_seed': SEED, 'auto_class_weights': 'Balanced'}
cfg = CFG()
train_data = pd.read_csv(os.path.join(cfg.HOME_DIR, 'train.csv'))
test_data = pd.read_csv(os.path.join(cfg.HOME_DIR, 'test.csv'))
greeks_data = pd.read_csv(os.path.join(cfg.HOME_DIR, 'greeks.csv'))
sample_data = pd.read_csv(os.path.join(cfg.HOME_DIR, 'sample_submission.csv'))
def rename_column(df):
df = df.rename(columns={'BD ': 'BD', 'CD ': 'CD', 'CW ': 'CW', 'FD ': 'FD'})
return df
def scale_features(train_df, test_df):
scaler = preprocessing.StandardScaler()
train_df[FEATURES] = scaler.fit_transform(train_df[FEATURES])
test_df[FEATURES] = scaler.transform(test_df[FEATURES])
return (train_df, test_df)
ej_mapper = {'A': 1, 'B': 0}
train_data['EJ'] = train_data.EJ.map(ej_mapper)
test_data['EJ'] = test_data.EJ.map(ej_mapper)
train_data = rename_column(train_data)
test_data = rename_column(test_data)
IDENTIFIER = 'Id'
FEATURES = ['AB', 'AF', 'AH', 'AM', 'AR', 'AX', 'AY', 'AZ', 'BC', 'BD', 'BN', 'BP', 'BQ', 'BR', 'BZ', 'CB', 'CC', 'CD', 'CF', 'CH', 'CL', 'CR', 'CS', 'CU', 'CW', 'DA', 'DE', 'DF', 'DH', 'DI', 'DL', 'DN', 'DU', 'DV', 'DY', 'EB', 'EE', 'EG', 'EH', 'EJ', 'EL', 'EP', 'EU', 'FC', 'FD', 'FE', 'FI', 'FL', 'FR', 'FS', 'GB', 'GE', 'GF', 'GH', 'GI', 'GL']
TARGET = 'Class'
skf = model_selection.StratifiedKFold(n_splits=cfg.SPLITS, shuffle=True, random_state=cfg.SEED)
train_data = train_data.sample(frac=1)
train_data['kfold'] = -99
train_data = train_data.reset_index(drop=True)
for fold, (tidx, vidx) in enumerate(skf.split(train_data[FEATURES], train_data[TARGET])):
train_data.loc[vidx, 'kfold'] = fold
def train_classifier(train_data, test_data, model_, fold):
df_train = train_data.query('kfold != @fold').reset_index(drop=True)
df_valid = train_data.query('kfold == @fold').reset_index(drop=True)
t_id = df_train[IDENTIFIER]
X_train = df_train[FEATURES]
y_train = df_train[TARGET]
v_id = df_valid[IDENTIFIER]
X_valid = df_valid[FEATURES]
y_valid = df_valid[TARGET]
v_test = test_data[IDENTIFIER]
X_test = test_data[FEATURES]
model_.fit(X_train, y_train)
v_pred = model_.predict_proba(X_valid)[:, 1]
test_pred = model_.predict_proba(X_test)[:, 1]
score = metrics.log_loss(y_valid, v_pred)
return (v_id, y_valid, v_pred, np.ones_like(v_pred, dtype='int8') * fold, test_pred)
model_0 = LGBMClassifier(**cfg.lgb_params)
oof_id = []
oof_tar = []
oof_pred = []
oof_fold = []
oof_sub = []
for fold in range(0, 5):
v_id, y_valid, v_pred, f, test_pred = train_classifier(train_data, test_data, model_0, fold)
oof_id.append(v_id)
oof_tar.append(y_valid)
oof_pred.append(v_pred)
oof_fold.append(f)
oof_sub.append(test_pred)
oof = np.concatenate(oof_pred)
true = np.concatenate(oof_tar)
names = np.concatenate(oof_id).reshape(-1)
folds = np.concatenate(oof_fold)
log_loss = metrics.log_loss(true, oof)
print('Overall OOF Log Loss with TTA = %.3f' % log_loss)
df_oof = pd.DataFrame(dict(id=names, Class=true, pred=oof, fold=folds))
df_oof.to_csv('oof_lgb.csv', index=False)
print(df_oof.head())
test_ids = test_data[IDENTIFIER].values.reshape(-1)
test_score = np.mean(np.column_stack(oof_sub), axis=1)
sub_oof = pd.DataFrame(dict(id=test_ids, Class=test_score))
sub_oof.to_csv('sub_lgb.csv', index=False)
print(sub_oof.head()) | code |
34120476/cell_21 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization
from keras.models import Sequential, Model
from keras.preprocessing import image
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
imagePaths.append(os.path.join(dirname, filename))
image = cv2.imread(imagePaths[0])
X_train.shape
X_cv.shape
X_test.shape
img_width = img_height = 224
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
X_train = X_train.reshape(X_train.shape[0], 3, img_width, img_height)
X_cv = X_cv.reshape(X_cv.shape[0], 3, img_width, img_height)
X_test = X_test.reshape(X_test.shape[0], 3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
X_train = X_train.reshape(X_train.shape[0], img_width, img_height, 3)
X_cv = X_cv.reshape(X_cv.shape[0], img_width, img_height, 3)
X_test = X_test.reshape(X_test.shape[0], img_width, img_height, 3)
def plt_dynamic(x, vy, ty, ax, colors=['b']):
pass
epoch = 25
batch = 32
num_classes = 3
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape, kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.25))
model.add(Dense(256, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.4))
model.add(Dense(128, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.3))
model.add(BatchNormalization())
model.add(Dense(64, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax', kernel_initializer='glorot_normal'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
his = model.fit(X_train, y_train, batch_size=batch, epochs=epoch, verbose=1, validation_data=(X_cv, y_cv))
fig, ax = plt.subplots(1, 1)
ax.set_xlabel('Epochs')
ax.set_ylabel('Binary Cross Entropy')
x = list(range(1, epoch + 1))
vy = his.history['val_loss']
ty = his.history['loss']
plt_dynamic(x, vy, ty, ax) | code |
34120476/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | X_cv.shape | code |
34120476/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.preprocessing import image
from keras.utils import np_utils
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
imagePaths.append(os.path.join(dirname, filename))
image = cv2.imread(imagePaths[0])
image.shape
Data = []
Target = []
resize = 224
for imagePath in tqdm(imagePaths):
label = imagePath.split(os.path.sep)[-2]
image = cv2.imread(imagePath)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (resize, resize)) / 255
Data.append(image)
Target.append(label)
encoder = LabelEncoder()
encoder.fit(Target)
encoded_Target = encoder.transform(Target)
encoded_Target = np_utils.to_categorical(encoded_Target)
encoded_Target[0] | code |
34120476/cell_25 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization
from keras.models import Sequential, Model
from keras.preprocessing import image
from keras.utils import np_utils
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
imagePaths.append(os.path.join(dirname, filename))
image = cv2.imread(imagePaths[0])
image.shape
Data = []
Target = []
resize = 224
for imagePath in tqdm(imagePaths):
label = imagePath.split(os.path.sep)[-2]
image = cv2.imread(imagePath)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (resize, resize)) / 255
Data.append(image)
Target.append(label)
df = pd.DataFrame(Target, columns=['Labels'])
encoder = LabelEncoder()
encoder.fit(Target)
encoded_Target = encoder.transform(Target)
encoded_Target = np_utils.to_categorical(encoded_Target)
encoder.classes_
X_train.shape
X_cv.shape
X_test.shape
img_width = img_height = 224
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
X_train = X_train.reshape(X_train.shape[0], 3, img_width, img_height)
X_cv = X_cv.reshape(X_cv.shape[0], 3, img_width, img_height)
X_test = X_test.reshape(X_test.shape[0], 3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
X_train = X_train.reshape(X_train.shape[0], img_width, img_height, 3)
X_cv = X_cv.reshape(X_cv.shape[0], img_width, img_height, 3)
X_test = X_test.reshape(X_test.shape[0], img_width, img_height, 3)
def plt_dynamic(x, vy, ty, ax, colors=['b']):
pass
epoch = 25
batch = 32
num_classes = 3
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape, kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.25))
model.add(Dense(256, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.4))
model.add(Dense(128, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.3))
model.add(BatchNormalization())
model.add(Dense(64, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax', kernel_initializer='glorot_normal'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
his = model.fit(X_train, y_train, batch_size=batch, epochs=epoch, verbose=1, validation_data=(X_cv, y_cv))
#Plotting Train and Validation Loss
fig,ax=plt.subplots(1,1)
ax.set_xlabel('Epochs')
ax.set_ylabel('Binary Cross Entropy')
x=list(range(1,epoch+1))
vy=his.history['val_loss']
ty=his.history['loss']
plt_dynamic(x,vy,ty,ax)
score = model.evaluate(X_test, y_test, verbose=0)
y_pred = model.predict(X_test).round()
encoder.classes_
x = confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))
Cm_df = pd.DataFrame(x, index=encoder.classes_, columns=encoder.classes_)
sns.set(font_scale=1.5, color_codes=True, palette='deep')
sns.heatmap(Cm_df, annot=True, annot_kws={'size': 16}, fmt='d', cmap='YlGnBu')
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
plt.title('Confusion Matrix') | code |
34120476/cell_4 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
imagePaths.append(os.path.join(dirname, filename))
image = cv2.imread(imagePaths[0])
image.shape | code |
34120476/cell_20 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization
from keras.models import Sequential, Model
X_train.shape
X_cv.shape
X_test.shape
img_width = img_height = 224
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
X_train = X_train.reshape(X_train.shape[0], 3, img_width, img_height)
X_cv = X_cv.reshape(X_cv.shape[0], 3, img_width, img_height)
X_test = X_test.reshape(X_test.shape[0], 3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
X_train = X_train.reshape(X_train.shape[0], img_width, img_height, 3)
X_cv = X_cv.reshape(X_cv.shape[0], img_width, img_height, 3)
X_test = X_test.reshape(X_test.shape[0], img_width, img_height, 3)
epoch = 25
batch = 32
num_classes = 3
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape, kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.25))
model.add(Dense(256, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.4))
model.add(Dense(128, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.3))
model.add(BatchNormalization())
model.add(Dense(64, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax', kernel_initializer='glorot_normal'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
his = model.fit(X_train, y_train, batch_size=batch, epochs=epoch, verbose=1, validation_data=(X_cv, y_cv)) | code |
34120476/cell_6 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
imagePaths.append(os.path.join(dirname, filename))
image = cv2.imread(imagePaths[0])
image.shape
Data = []
Target = []
resize = 224
for imagePath in tqdm(imagePaths):
label = imagePath.split(os.path.sep)[-2]
image = cv2.imread(imagePath)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (resize, resize)) / 255
Data.append(image)
Target.append(label)
df = pd.DataFrame(Target, columns=['Labels'])
sns.countplot(df['Labels']) | code |
34120476/cell_2 | [
"text_plain_output_1.png"
] | import cv2
import os
import keras
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from sklearn.metrics import confusion_matrix
from keras.preprocessing import image
from keras import models
from keras import layers
from keras import optimizers
from keras import applications
from keras.optimizers import Adam
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D, Conv2D, MaxPooling2D, BatchNormalization
from keras import backend as k
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping
from tqdm import tqdm
from keras.optimizers import SGD
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
from keras import backend as K | code |
34120476/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization
from keras.models import Sequential, Model
X_train.shape
X_cv.shape
X_test.shape
img_width = img_height = 224
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
X_train = X_train.reshape(X_train.shape[0], 3, img_width, img_height)
X_cv = X_cv.reshape(X_cv.shape[0], 3, img_width, img_height)
X_test = X_test.reshape(X_test.shape[0], 3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
X_train = X_train.reshape(X_train.shape[0], img_width, img_height, 3)
X_cv = X_cv.reshape(X_cv.shape[0], img_width, img_height, 3)
X_test = X_test.reshape(X_test.shape[0], img_width, img_height, 3)
epoch = 25
batch = 32
num_classes = 3
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape, kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.25))
model.add(Dense(256, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.4))
model.add(Dense(128, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.3))
model.add(BatchNormalization())
model.add(Dense(64, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax', kernel_initializer='glorot_normal'))
model.summary() | code |
34120476/cell_8 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from keras.utils import np_utils
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
imagePaths.append(os.path.join(dirname, filename))
image = cv2.imread(imagePaths[0])
image.shape
Data = []
Target = []
resize = 224
for imagePath in tqdm(imagePaths):
label = imagePath.split(os.path.sep)[-2]
image = cv2.imread(imagePath)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (resize, resize)) / 255
Data.append(image)
Target.append(label)
encoder = LabelEncoder()
encoder.fit(Target)
encoded_Target = encoder.transform(Target)
encoded_Target = np_utils.to_categorical(encoded_Target)
encoder.classes_ | code |
34120476/cell_3 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
imagePaths.append(os.path.join(dirname, filename))
image = cv2.imread(imagePaths[0])
plt.imshow(image) | code |
34120476/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | X_train.shape
X_cv.shape
X_test.shape
img_width = img_height = 224
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
X_train = X_train.reshape(X_train.shape[0], 3, img_width, img_height)
X_cv = X_cv.reshape(X_cv.shape[0], 3, img_width, img_height)
X_test = X_test.reshape(X_test.shape[0], 3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
X_train = X_train.reshape(X_train.shape[0], img_width, img_height, 3)
X_cv = X_cv.reshape(X_cv.shape[0], img_width, img_height, 3)
X_test = X_test.reshape(X_test.shape[0], img_width, img_height, 3)
X_train[0] | code |
34120476/cell_24 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from keras.utils import np_utils
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
imagePaths.append(os.path.join(dirname, filename))
image = cv2.imread(imagePaths[0])
image.shape
Data = []
Target = []
resize = 224
for imagePath in tqdm(imagePaths):
label = imagePath.split(os.path.sep)[-2]
image = cv2.imread(imagePath)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (resize, resize)) / 255
Data.append(image)
Target.append(label)
encoder = LabelEncoder()
encoder.fit(Target)
encoded_Target = encoder.transform(Target)
encoded_Target = np_utils.to_categorical(encoded_Target)
encoder.classes_
encoder.classes_ | code |
34120476/cell_14 | [
"text_plain_output_1.png"
] | X_test.shape | code |
34120476/cell_22 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization
from keras.models import Sequential, Model
X_train.shape
X_cv.shape
X_test.shape
img_width = img_height = 224
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
X_train = X_train.reshape(X_train.shape[0], 3, img_width, img_height)
X_cv = X_cv.reshape(X_cv.shape[0], 3, img_width, img_height)
X_test = X_test.reshape(X_test.shape[0], 3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
X_train = X_train.reshape(X_train.shape[0], img_width, img_height, 3)
X_cv = X_cv.reshape(X_cv.shape[0], img_width, img_height, 3)
X_test = X_test.reshape(X_test.shape[0], img_width, img_height, 3)
epoch = 25
batch = 32
num_classes = 3
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape, kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.25))
model.add(Dense(256, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.4))
model.add(Dense(128, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.3))
model.add(BatchNormalization())
model.add(Dense(64, activation='relu', kernel_initializer='he_normal'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax', kernel_initializer='glorot_normal'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
his = model.fit(X_train, y_train, batch_size=batch, epochs=epoch, verbose=1, validation_data=(X_cv, y_cv))
score = model.evaluate(X_test, y_test, verbose=0)
print('The test accuracy for the model is %f ' % (score[1] * 100)) | code |
34120476/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | X_train.shape | code |
34120476/cell_5 | [
"image_output_1.png"
] | from keras.preprocessing import image
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
imagePaths.append(os.path.join(dirname, filename))
image = cv2.imread(imagePaths[0])
image.shape
Data = []
Target = []
resize = 224
for imagePath in tqdm(imagePaths):
label = imagePath.split(os.path.sep)[-2]
image = cv2.imread(imagePath)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (resize, resize)) / 255
Data.append(image)
Target.append(label) | code |
73093128/cell_21 | [
"text_html_output_1.png"
] | from geopandas.tools import geocode
import folium
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas.tools import geocode
import folium
agent = 'my_colls_app'
def geo_short(location):
"""
Take address, cross-street, etc. and return geocoded point at which
lat/long can conveniently accessed. Uses Nominatim.
"""
pt = geocode(location, provider='nominatim', user_agent=agent)
return pt.geometry.iloc[0]
def basemap_with_buffer(location, buffer_radius_miles):
centerpoint = geo_short(location)
basemap = folium.Map(location=[centerpoint.y, centerpoint.x], tiles='openstreetmap', zoom_start=15)
buffer_radius_meters = buffer_radius_miles * 5280 / 3.28084
basemap_buffer = folium.Circle(location=[centerpoint.y, centerpoint.x], radius=buffer_radius_meters).add_to(basemap)
return basemap
patco_address = '100 Lees Ave, Collingswood, NJ 08108'
patco_base = basemap_with_buffer(patco_address, 0.5)
patco_base | code |
73093128/cell_23 | [
"text_html_output_1.png"
] | from geopandas.tools import geocode
import folium
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas.tools import geocode
import folium
agent = 'my_colls_app'
ramp_path = '../input/rampdatalog/RampDataLog.xlsm'
ramp_df = pd.read_excel(ramp_path, sheet_name='data')
ramp_df.rename(columns={'Cross Street 1': 'CS_1', 'Cross Street 2': 'CS_2'}, inplace=True)
ramp_df.replace(' NE', 'NE', inplace=True)
ramp_df.Notes.fillna('None', inplace=True)
import math
def cs_comb_str(cs1, cs2):
"""
Take an intersection's cross streets as arguments and return
a string of the complete location
"""
inter = ''
suffixes = ['Ave', 'Ln', 'Terr']
if 'btw' in cs1:
inter = cs1.split('btw')[0] + 'Ave btw ' + cs2
elif type(cs2) != str:
inter = cs1
elif '(' in cs2:
inter = cs1 + ' Ave and ' + cs2.split()[0] + ' Ave ' + cs2.split()[1]
else:
if not any([suf in cs1 for suf in suffixes]):
cs1 += ' Ave'
if not any([suf in cs2 for suf in suffixes]) and (not any([landmark in cs2 for landmark in ['alleyway', 'exit']])) and (len(cs2.split()[-1]) > 1):
cs2 += ' Ave'
inter = ' and '.join([cs1, cs2])
return inter
ramp_df['Inter'] = ramp_df.apply(lambda row: cs_comb_str(row['CS_1'], row['CS_2']), axis=1)
cols = ramp_df.columns.tolist()
cols = cols[:3] + cols[-2:] + cols[3:-2]
ramp_df = ramp_df[cols]
ramp_df.to_csv('CollingswoodADA_LatLong_checkpoint.csv', index=False)
ramp_df = pd.read_csv('../input/collingswoodada-clean-latlong/CollingswoodADA_LatLong_checkpoint.csv')
def geo_short(location):
"""
Take address, cross-street, etc. and return geocoded point at which
lat/long can conveniently accessed. Uses Nominatim.
"""
pt = geocode(location, provider='nominatim', user_agent=agent)
return pt.geometry.iloc[0]
def basemap_with_buffer(location, buffer_radius_miles):
centerpoint = geo_short(location)
basemap = folium.Map(location=[centerpoint.y, centerpoint.x], tiles='openstreetmap', zoom_start=15)
buffer_radius_meters = buffer_radius_miles * 5280 / 3.28084
basemap_buffer = folium.Circle(location=[centerpoint.y, centerpoint.x], radius=buffer_radius_meters).add_to(basemap)
return basemap
patco_address = '100 Lees Ave, Collingswood, NJ 08108'
patco_base = basemap_with_buffer(patco_address, 0.5)
patco_base
patco_base = basemap_with_buffer(patco_address, 0.5)
for i, location in ramp_df.iterrows():
if not np.isnan(location.Lat) and (not np.isnan(location.Long)):
folium.Marker(location=[location.Lat, location.Long], tooltip=location.Inter).add_to(patco_base)
patco_base
from folium.plugins import MarkerCluster
patco_base = basemap_with_buffer(patco_address, 0.5)
marker_cluster = folium.plugins.MarkerCluster()
for i, location in ramp_df.iterrows():
if not np.isnan(location.Lat) and (not np.isnan(location.Long)):
marker_cluster.add_child(folium.Marker([location.Lat, location.Long], tooltip=location.Inter))
patco_base.add_child(marker_cluster)
patco_base | code |
73093128/cell_1 | [
"text_plain_output_1.png"
] | !pip install openpyxl | code |
73093128/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
ramp_path = '../input/rampdatalog/RampDataLog.xlsm'
ramp_df = pd.read_excel(ramp_path, sheet_name='data')
ramp_df.rename(columns={'Cross Street 1': 'CS_1', 'Cross Street 2': 'CS_2'}, inplace=True)
ramp_df.replace(' NE', 'NE', inplace=True)
ramp_df.Notes.fillna('None', inplace=True)
import math
def cs_comb_str(cs1, cs2):
"""
Take an intersection's cross streets as arguments and return
a string of the complete location
"""
inter = ''
suffixes = ['Ave', 'Ln', 'Terr']
if 'btw' in cs1:
inter = cs1.split('btw')[0] + 'Ave btw ' + cs2
elif type(cs2) != str:
inter = cs1
elif '(' in cs2:
inter = cs1 + ' Ave and ' + cs2.split()[0] + ' Ave ' + cs2.split()[1]
else:
if not any([suf in cs1 for suf in suffixes]):
cs1 += ' Ave'
if not any([suf in cs2 for suf in suffixes]) and (not any([landmark in cs2 for landmark in ['alleyway', 'exit']])) and (len(cs2.split()[-1]) > 1):
cs2 += ' Ave'
inter = ' and '.join([cs1, cs2])
return inter
ramp_df['Inter'] = ramp_df.apply(lambda row: cs_comb_str(row['CS_1'], row['CS_2']), axis=1)
cols = ramp_df.columns.tolist()
cols = cols[:3] + cols[-2:] + cols[3:-2]
ramp_df = ramp_df[cols]
ramp_df.to_csv('CollingswoodADA_LatLong_checkpoint.csv', index=False)
ramp_df = pd.read_csv('../input/collingswoodada-clean-latlong/CollingswoodADA_LatLong_checkpoint.csv')
ramp_df.head() | code |
73093128/cell_24 | [
"text_html_output_1.png"
] | from geopandas.tools import geocode
import folium
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas.tools import geocode
import folium
agent = 'my_colls_app'
ramp_path = '../input/rampdatalog/RampDataLog.xlsm'
ramp_df = pd.read_excel(ramp_path, sheet_name='data')
ramp_df.rename(columns={'Cross Street 1': 'CS_1', 'Cross Street 2': 'CS_2'}, inplace=True)
ramp_df.replace(' NE', 'NE', inplace=True)
ramp_df.Notes.fillna('None', inplace=True)
import math
def cs_comb_str(cs1, cs2):
"""
Take an intersection's cross streets as arguments and return
a string of the complete location
"""
inter = ''
suffixes = ['Ave', 'Ln', 'Terr']
if 'btw' in cs1:
inter = cs1.split('btw')[0] + 'Ave btw ' + cs2
elif type(cs2) != str:
inter = cs1
elif '(' in cs2:
inter = cs1 + ' Ave and ' + cs2.split()[0] + ' Ave ' + cs2.split()[1]
else:
if not any([suf in cs1 for suf in suffixes]):
cs1 += ' Ave'
if not any([suf in cs2 for suf in suffixes]) and (not any([landmark in cs2 for landmark in ['alleyway', 'exit']])) and (len(cs2.split()[-1]) > 1):
cs2 += ' Ave'
inter = ' and '.join([cs1, cs2])
return inter
ramp_df['Inter'] = ramp_df.apply(lambda row: cs_comb_str(row['CS_1'], row['CS_2']), axis=1)
cols = ramp_df.columns.tolist()
cols = cols[:3] + cols[-2:] + cols[3:-2]
ramp_df = ramp_df[cols]
ramp_df.to_csv('CollingswoodADA_LatLong_checkpoint.csv', index=False)
ramp_df = pd.read_csv('../input/collingswoodada-clean-latlong/CollingswoodADA_LatLong_checkpoint.csv')
def geo_short(location):
"""
Take address, cross-street, etc. and return geocoded point at which
lat/long can conveniently accessed. Uses Nominatim.
"""
pt = geocode(location, provider='nominatim', user_agent=agent)
return pt.geometry.iloc[0]
def basemap_with_buffer(location, buffer_radius_miles):
centerpoint = geo_short(location)
basemap = folium.Map(location=[centerpoint.y, centerpoint.x], tiles='openstreetmap', zoom_start=15)
buffer_radius_meters = buffer_radius_miles * 5280 / 3.28084
basemap_buffer = folium.Circle(location=[centerpoint.y, centerpoint.x], radius=buffer_radius_meters).add_to(basemap)
return basemap
patco_address = '100 Lees Ave, Collingswood, NJ 08108'
patco_base = basemap_with_buffer(patco_address, 0.5)
patco_base
patco_base = basemap_with_buffer(patco_address, 0.5)
for i, location in ramp_df.iterrows():
if not np.isnan(location.Lat) and (not np.isnan(location.Long)):
folium.Marker(location=[location.Lat, location.Long], tooltip=location.Inter).add_to(patco_base)
patco_base
from folium.plugins import MarkerCluster
patco_base = basemap_with_buffer(patco_address, 0.5)
marker_cluster = folium.plugins.MarkerCluster()
for i, location in ramp_df.iterrows():
if not np.isnan(location.Lat) and (not np.isnan(location.Long)):
marker_cluster.add_child(folium.Marker([location.Lat, location.Long], tooltip=location.Inter))
patco_base.add_child(marker_cluster)
patco_base
patco_base = basemap_with_buffer(patco_address, 0.5)
for i, location in ramp_df.iterrows():
if not np.isnan(location.Lat) and (not np.isnan(location.Long)):
color = 'green' if location.Compliance == 'Y' else 'red'
folium.Circle(location=[location.Lat, location.Long], radius=10, color=color, tooltip=location.Inter).add_to(patco_base)
patco_base | code |
73093128/cell_22 | [
"text_html_output_1.png"
] | from geopandas.tools import geocode
import folium
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas.tools import geocode
import folium
agent = 'my_colls_app'
ramp_path = '../input/rampdatalog/RampDataLog.xlsm'
ramp_df = pd.read_excel(ramp_path, sheet_name='data')
ramp_df.rename(columns={'Cross Street 1': 'CS_1', 'Cross Street 2': 'CS_2'}, inplace=True)
ramp_df.replace(' NE', 'NE', inplace=True)
ramp_df.Notes.fillna('None', inplace=True)
import math
def cs_comb_str(cs1, cs2):
"""
Take an intersection's cross streets as arguments and return
a string of the complete location
"""
inter = ''
suffixes = ['Ave', 'Ln', 'Terr']
if 'btw' in cs1:
inter = cs1.split('btw')[0] + 'Ave btw ' + cs2
elif type(cs2) != str:
inter = cs1
elif '(' in cs2:
inter = cs1 + ' Ave and ' + cs2.split()[0] + ' Ave ' + cs2.split()[1]
else:
if not any([suf in cs1 for suf in suffixes]):
cs1 += ' Ave'
if not any([suf in cs2 for suf in suffixes]) and (not any([landmark in cs2 for landmark in ['alleyway', 'exit']])) and (len(cs2.split()[-1]) > 1):
cs2 += ' Ave'
inter = ' and '.join([cs1, cs2])
return inter
ramp_df['Inter'] = ramp_df.apply(lambda row: cs_comb_str(row['CS_1'], row['CS_2']), axis=1)
cols = ramp_df.columns.tolist()
cols = cols[:3] + cols[-2:] + cols[3:-2]
ramp_df = ramp_df[cols]
ramp_df.to_csv('CollingswoodADA_LatLong_checkpoint.csv', index=False)
ramp_df = pd.read_csv('../input/collingswoodada-clean-latlong/CollingswoodADA_LatLong_checkpoint.csv')
def geo_short(location):
"""
Take address, cross-street, etc. and return geocoded point at which
lat/long can conveniently accessed. Uses Nominatim.
"""
pt = geocode(location, provider='nominatim', user_agent=agent)
return pt.geometry.iloc[0]
def basemap_with_buffer(location, buffer_radius_miles):
centerpoint = geo_short(location)
basemap = folium.Map(location=[centerpoint.y, centerpoint.x], tiles='openstreetmap', zoom_start=15)
buffer_radius_meters = buffer_radius_miles * 5280 / 3.28084
basemap_buffer = folium.Circle(location=[centerpoint.y, centerpoint.x], radius=buffer_radius_meters).add_to(basemap)
return basemap
patco_address = '100 Lees Ave, Collingswood, NJ 08108'
patco_base = basemap_with_buffer(patco_address, 0.5)
patco_base
patco_base = basemap_with_buffer(patco_address, 0.5)
for i, location in ramp_df.iterrows():
if not np.isnan(location.Lat) and (not np.isnan(location.Long)):
folium.Marker(location=[location.Lat, location.Long], tooltip=location.Inter).add_to(patco_base)
patco_base | code |
18161386/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
18161386/cell_3 | [
"image_output_11.png",
"text_plain_output_100.png",
"image_output_98.png",
"text_plain_output_201.png",
"text_plain_output_84.png",
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"image_output_5.png",
"image_output_48.png",
"image_output_68.png",
"text_plain_output_171.png",
"image_output_75.png",
"text_plain_output_14.png",
"image_output_18.png",
"text_plain_output_159.png",
"text_plain_output_32.png",
"text_plain_output_88.png",
"text_plain_output_29.png",
"image_output_58.png",
"text_plain_output_140.png",
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"image_output_7.png",
"text_plain_output_153.png",
"text_plain_output_170.png",
"text_plain_output_92.png",
"text_plain_output_57.png",
"text_plain_output_120.png",
"image_output_62.png",
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"text_plain_output_21.png",
"text_plain_output_104.png",
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"image_output_31.png",
"text_plain_output_47.png",
"text_plain_output_121.png",
"text_plain_output_25.png",
"text_plain_output_134.png",
"text_plain_output_77.png",
"image_output_65.png",
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"text_plain_output_183.png",
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"text_plain_output_50.png",
"text_plain_output_36.png",
"image_output_32.png",
"image_output_53.png",
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"text_plain_output_87.png",
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"text_plain_output_165.png",
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"image_output_57.png",
"text_plain_output_7.png",
"text_plain_output_166.png",
"image_output_36.png",
"text_plain_output_91.png",
"image_output_8.png",
"image_output_37.png",
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"image_output_16.png",
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"text_plain_output_71.png",
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"text_plain_output_162.png",
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"text_plain_output_127.png",
"image_output_89.png",
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"text_plain_output_196.png",
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"image_output_94.png",
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"image_output_43.png",
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"image_output_1.png",
"text_plain_output_105.png",
"text_plain_output_80.png",
"image_output_10.png",
"text_plain_output_94.png",
"text_plain_output_164.png",
"text_plain_output_124.png",
"text_plain_output_17.png",
"text_plain_output_148.png",
"text_plain_output_11.png",
"image_output_88.png",
"text_plain_output_12.png",
"image_output_33.png",
"text_plain_output_194.png",
"text_plain_output_62.png",
"image_output_87.png",
"image_output_50.png",
"text_plain_output_95.png",
"image_output_15.png",
"image_output_99.png",
"image_output_49.png",
"image_output_100.png",
"text_plain_output_156.png",
"text_plain_output_61.png",
"image_output_76.png",
"image_output_9.png",
"text_plain_output_83.png",
"image_output_19.png",
"image_output_79.png",
"image_output_61.png",
"image_output_38.png",
"text_plain_output_135.png",
"image_output_26.png",
"text_plain_output_46.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/data.csv')
data.info() | code |
18161386/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
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)
data = pd.read_csv('../input/data.csv')
data.drop(['id', 'Unnamed: 32'], axis=1, inplace=True)
x_data = data.drop(['diagnosis'], axis=1)
data.diagnosis = [1 if each == 'M' else 0 for each in data.diagnosis]
x = ((x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data))).values
y = data.diagnosis.values
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
x_train = x_train.T
x_test = x_test.T
y_train = y_train.T
y_test = y_test.T
def initialize_parameters(dimension):
theta0 = 0.0
thetaLeft = np.full((dimension, 1), 0.01)
return (theta0, thetaLeft)
def sigmoid(z):
return 1 / (1 + np.exp(-z))
def forward_backward_propagation(theta0, thetaLeft, x_train, y_train):
z = theta0 + np.dot(thetaLeft.T, x_train)
y_head = sigmoid(z)
cost_function = -y_train * np.log(y_head) - (1 - y_head) * np.log(1 - y_head)
cost = np.sum(cost_function) / x_train.shape[1]
derivative_theta0 = np.sum(y_head - y_train) / x_train.shape[1]
derivative_thetaLeft = np.dot(x_train, (y_head - y_train).T) / x_train.shape[1]
gradients = {'derivative_theta0': derivative_theta0, 'derivative_thetaLeft': derivative_thetaLeft}
return (cost, gradients)
def update(theta0, thetaLeft, x_train, y_train, learning_rate, number_of_iteration):
cost_list = []
cost_list_to_plot = []
index_to_plot = []
for i in range(number_of_iteration):
cost, gradients = forward_backward_propagation(theta0, thetaLeft, x_train, y_train)
cost_list.append(cost)
theta0 = theta0 - learning_rate * gradients['derivative_theta0']
thetaLeft = thetaLeft - learning_rate * gradients['derivative_thetaLeft']
if i % 50 == 0:
cost_list_to_plot.append(cost)
index_to_plot.append(i)
parameters = {'theta0': theta0, 'thetaLeft': thetaLeft}
plt.xticks(index_to_plot, rotation='vertical')
return (parameters, gradients, cost_list)
def predict(theta0, thetaLeft, x_test):
z = sigmoid(theta0 + np.dot(thetaLeft.T, x_test))
Y_prediction = np.zeros((1, x_test.shape[1]))
for i in range(z.shape[1]):
if z[0, i] <= 0.5:
Y_prediction[0, i] = 0
else:
Y_prediction[0, i] = 1
return Y_prediction
def logistic_regression(x_train, y_train, x_test, y_test, learning_rate, number_of_iteration):
dimension = x_train.shape[0]
theta0, thetaLeft = initialize_parameters(dimension)
parameters, gradients, cost_list = update(theta0, thetaLeft, x_train, y_train, learning_rate, number_of_iteration)
y_prediction_test = predict(parameters['theta0'], parameters['thetaLeft'], x_test)
y_prediction_train = predict(parameters['theta0'], parameters['thetaLeft'], x_train)
cost_list_global_train.append(100 - np.mean(np.abs(y_prediction_train - y_train)) * 100)
cost_list_global_test.append(100 - np.mean(np.abs(y_prediction_test - y_test)) * 100)
logistic_regression(x_train, y_train, x_test, y_test, learning_rate=0.01, number_of_iteration=500) | code |
32070024/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year').suicide_rate.mean().idxmax() | code |
32070024/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year').suicide_rate.mean().idxmax()
suicide.groupby('age').suicide_rate.mean().sort_values().plot.barh()
suicide.groupby('country').size()
suicide.groupby('country').suicide_rate.mean().sort_values(ascending=False).head(10)
Hungary = suicide.set_index('country').loc['Hungary']
Hungary
suicide.groupby('year').suicide_rate.mean().plot.bar() | code |
32070024/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year').suicide_rate.mean().idxmax()
suicide.groupby('age').suicide_rate.mean().sort_values().plot.barh()
suicide.groupby('country').size()
suicide.groupby('country').suicide_rate.mean().sort_values(ascending=False).head(10)
Hungary = suicide.set_index('country').loc['Hungary']
Hungary
Hungary.groupby(['age', 'year', 'sex']).suicide_rate.mean().sort_values().tail(10).plot.barh() | code |
32070024/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 |
32070024/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year').suicide_rate.mean().idxmax()
suicide.groupby('age').suicide_rate.mean().sort_values().plot.barh()
suicide.groupby('country').size()
suicide.groupby('country').suicide_rate.mean().sort_values(ascending=False).head(10)
Hungary = suicide.set_index('country').loc['Hungary']
Hungary | code |
32070024/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean() | code |
32070024/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year').suicide_rate.mean().idxmax()
suicide.groupby('age').suicide_rate.mean().sort_values().plot.barh()
suicide.groupby('country').size()
suicide.groupby('country').suicide_rate.mean().sort_values(ascending=False).head(10) | code |
32070024/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide.head() | code |
32070024/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year').suicide_rate.mean().idxmax()
suicide.groupby('age').suicide_rate.mean().sort_values().plot.barh() | code |
32070024/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year').suicide_rate.mean().idxmax()
suicide.groupby('age').suicide_rate.mean().sort_values().plot.barh()
suicide.groupby('country').size() | code |
32070024/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist() | code |
74052380/cell_21 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
classifier = Sequential()
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
y_test = y_test.astype('float64')
y_train = y_train.astype('float64')
classifierHistory = classifier.fit(X_train, y_train, batch_size=64, epochs=70, validation_data=(X_test, y_test))
classifier = Sequential()
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
classifierHistory = classifier.fit(X_train, y_train, batch_size=64, epochs=70, validation_data=(X_test, y_test))
plt.plot(classifierHistory.history['accuracy'])
plt.plot(classifierHistory.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show() | code |
74052380/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('/kaggle/input/heart-disease-uci/heart.csv')
dataset.head() | code |
74052380/cell_23 | [
"image_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
classifier = Sequential()
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
y_test = y_test.astype('float64')
y_train = y_train.astype('float64')
classifierHistory = classifier.fit(X_train, y_train, batch_size=64, epochs=70, validation_data=(X_test, y_test))
classifier = Sequential()
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
classifierHistory = classifier.fit(X_train, y_train, batch_size=64, epochs=70, validation_data=(X_test, y_test))
plt.plot(classifierHistory.history['loss'])
plt.plot(classifierHistory.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show() | code |
74052380/cell_19 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
classifier = Sequential()
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
y_test = y_test.astype('float64')
y_train = y_train.astype('float64')
classifierHistory = classifier.fit(X_train, y_train, batch_size=64, epochs=70, validation_data=(X_test, y_test))
classifier = Sequential()
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
classifierHistory = classifier.fit(X_train, y_train, batch_size=64, epochs=70, validation_data=(X_test, y_test))
scores = classifier.evaluate(X_test, y_test)
print('Accuracy: %.2f%%' % (scores[1] * 100)) | code |
74052380/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 |
74052380/cell_15 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
classifier = Sequential()
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
y_test = y_test.astype('float64')
y_train = y_train.astype('float64')
classifierHistory = classifier.fit(X_train, y_train, batch_size=64, epochs=70, validation_data=(X_test, y_test)) | code |
74052380/cell_17 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
classifier = Sequential()
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
y_test = y_test.astype('float64')
y_train = y_train.astype('float64')
classifierHistory = classifier.fit(X_train, y_train, batch_size=64, epochs=70, validation_data=(X_test, y_test))
classifier = Sequential()
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=12, kernel_initializer='uniform', activation='relu'))
classifier.add(Dropout(0.1))
classifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
classifierHistory = classifier.fit(X_train, y_train, batch_size=64, epochs=70, validation_data=(X_test, y_test)) | code |
16157465/cell_4 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
def read_xyz(path, filename):
return pd.read_csv(path + filename, skiprows=2, header=None, sep=' ', usecols=[0, 1, 2, 3], names=['atom', 'x', 'y', 'z'])
path = '../input/structures/'
filename = 'dsgdb9nsd_000001.xyz'
read_xyz(path, filename) | code |
16157465/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | """
atom_list = []
for filename in os.listdir("../input/structures"):
atom_list = atom_list + list(read_xyz(path, filename)['atom'])
atom_list = set(atom_list)
print(atom_list)
"""
print("{'O', 'H', 'C', 'F', 'N'}") | code |
16157465/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
train.head() | code |
16157465/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
def read_xyz(path, filename):
return pd.read_csv(path + filename, skiprows=2, header=None, sep=' ', usecols=[0, 1, 2, 3], names=['atom', 'x', 'y', 'z'])
path = '../input/structures/'
filename = 'dsgdb9nsd_000001.xyz'
read_xyz(path, filename)
x_list = []
y_list = []
z_list = []
for filename in os.listdir('../input/structures'):
x_list = x_list + list(read_xyz(path, filename)['x'])
y_list = y_list + list(read_xyz(path, filename)['y'])
z_list = z_list + list(read_xyz(path, filename)['z'])
dimfig, dimaxes = plt.subplots(3, 1, figsize=(6, 6))
sns.distplot(x_list, ax=dimaxes[0])
sns.distplot(y_list, ax=dimaxes[1])
sns.distplot(z_list, ax=dimaxes[2])
print('x max: ' + str(np.max(x_list)) + ' x min : ' + str(np.min(x_list)))
print('y max: ' + str(np.max(y_list)) + ' y min : ' + str(np.min(y_list)))
print('z max: ' + str(np.max(z_list)) + ' z min : ' + str(np.min(z_list))) | code |
16157465/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
def read_xyz(path, filename):
return pd.read_csv(path + filename, skiprows=2, header=None, sep=' ', usecols=[0, 1, 2, 3], names=['atom', 'x', 'y', 'z'])
path = '../input/structures/'
filename = 'dsgdb9nsd_000001.xyz'
read_xyz(path, filename)
x_list = []
y_list = []
z_list = []
for filename in os.listdir("../input/structures"):
x_list = x_list + list(read_xyz(path, filename)['x'])
y_list = y_list + list(read_xyz(path, filename)['y'])
z_list = z_list + list(read_xyz(path, filename)['z'])
dimfig, dimaxes = plt.subplots(3, 1, figsize = (6, 6))
sns.distplot(x_list, ax=dimaxes[0])
sns.distplot(y_list, ax=dimaxes[1])
sns.distplot(z_list, ax=dimaxes[2])
print("x max: " + str(np.max(x_list)) + " x min : " + str(np.min(x_list)))
print("y max: " + str(np.max(y_list)) + " y min : " + str(np.min(y_list)))
print("z max: " + str(np.max(z_list)) + " z min : " + str(np.min(z_list)))
coupling_types = set(train['type'])
coupling_types = list(coupling_types)
totals = [np.sum(train['type'] == x) for x in coupling_types]
subsets = dict()
for x in coupling_types:
subsets[x] = train.loc[train['type'] == x]
bar_fig, bar_axis = plt.subplots()
sns.barplot(coupling_types, totals, ax = bar_axis)
dist_fig, dist_axes = plt.subplots(len(subsets), 1, figsize = (6, 12))
for (x, y) in zip(dist_axes, coupling_types):
sns.distplot(subsets[y]['scalar_coupling_constant'], ax=x)
x.set_title(y)
dist_fig.tight_layout()
def length(data, index1, index2):
"""Takes an xyz file imported by read_xyz and calculates the distance between two points"""
return np.sqrt(np.sum(np.square(data[['x', 'y', 'z']].loc[index1] - data[['x', 'y', 'z']].loc[index2])))
def neighbours(data, index):
"""Takes an xyz file imported by read_xyz and calculates the number of neighbours within sqrt(3) Å of the indexed atom"""
l2 = np.array([np.sum(np.square(data[['x', 'y', 'z']].loc[index] - data[['x', 'y', 'z']].loc[x])) for x in range(len(data))])
return np.sum(l2 < 3) - 1
def nearest(data, index):
"""Takes an xyz file imported by read_xyz and finds the index of the nearest atom"""
point = data.loc[index][['x', 'y', 'z']]
data = data[data['atom'] != 'H'][['x', 'y', 'z']]
data[['x', 'y', 'z']] = data[['x', 'y', 'z']] - point
data[['x', 'y', 'z']] = np.square(data[['x', 'y', 'z']])
data = np.sum(data, axis=1)
if index in data.index:
data[index] = 999
return np.argmin(data)
def magnitude(vector):
"""Calculates the magnitude of a vector"""
return np.sqrt(np.sum(np.square(vector)))
def dihedral(point1, point2, point3, point4):
"""Calculates the dihederal angle between two bonds"""
b1 = point1 - point2
b2 = point2 - point3
b3 = point3 - point4
n1 = np.cross(b1, b2)
n1 = n1 / magnitude(n1)
n2 = np.cross(b2, b3)
n2 = n2 / magnitude(n2)
m1 = np.cross(n1, b2 / magnitude(b2))
x = np.dot(n1, n2)
y = np.dot(m1, n2)
return np.arctan2(x, y)
def single_bond(coupling_type):
feature_list = []
for x in range(1000):
current = subsets[coupling_type].iloc[x]
index0 = current['atom_index_0']
index1 = current['atom_index_1']
filename = current['molecule_name'] + '.xyz'
data = read_xyz(path, filename)
feature_list.append((length(data, index0, index1), neighbours(data, index1), current['scalar_coupling_constant']))
return pd.DataFrame(feature_list, columns=['length', 'hybrid', 'coupling'])
def two_bond(coupling_type):
feature_list = []
for x in range(1000):
current = subsets[coupling_type].iloc[x]
data = read_xyz(path, current['molecule_name'] + '.xyz')
index_0 = current['atom_index_0']
index_1 = current['atom_index_1']
shared = nearest(data, index_0)
length1 = length(data, index_0, shared)
length2 = length(data, index_1, shared)
vector1 = data[['x', 'y', 'z']].loc[index_0] - data[['x', 'y', 'z']].loc[shared]
vector2 = data[['x', 'y', 'z']].loc[index_1] - data[['x', 'y', 'z']].loc[shared]
cosine = np.dot(vector1, vector2) / (length1 * length2)
shared_hybrid = neighbours(data, shared)
carbon_hybrid = neighbours(data, index_1)
feature_list.append((length1, length2, cosine, data['atom'].iloc[shared], shared_hybrid, carbon_hybrid, current['scalar_coupling_constant']))
return pd.DataFrame(feature_list, columns=['length1', 'length2', 'cosine', 'atom', 'hybrid1', 'hybrid2', 'coupling'])
def three_bond(coupling_type):
feature_list = []
for x in range(1000):
current = subsets[coupling_type].iloc[x]
data = read_xyz(path, current['molecule_name'] + '.xyz')
index_0 = current['atom_index_0']
index_1 = current['atom_index_1']
shared1 = nearest(data, index_0)
shared2 = nearest(data, index_1)
length1 = length(data, index_0, shared1)
length2 = length(data, index_1, shared2)
length_shared = length(data, index_0, index_1)
cosine = dihedral(data[['x', 'y', 'z']].loc[index_0], data[['x', 'y', 'z']].loc[shared1], data[['x', 'y', 'z']].loc[shared2], data[['x', 'y', 'z']].loc[index_1])
shared1_hybrid = neighbours(data, shared1)
shared2_hybrid = neighbours(data, shared2)
terminal_hybrid = neighbours(data, index_1)
feature_list.append((length1, length2, length_shared, cosine, data['atom'].iloc[shared1], data['atom'].iloc[shared2], shared1_hybrid, shared2_hybrid, terminal_hybrid, current['scalar_coupling_constant']))
return pd.DataFrame(feature_list, columns=['length1', 'length2', 'length_shared', 'angle', 'atom1', 'atom2', 'hybrid1', 'hybrid2', 'terminal_hybrid', 'coupling'])
function_dict = {'1': single_bond, '2': two_bond, '3': three_bond}
engineered = {x: function_dict[x[0]](x) for x in coupling_types} | code |
16157465/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
coupling_types = set(train['type'])
print(coupling_types) | code |
16157465/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
def read_xyz(path, filename):
return pd.read_csv(path + filename, skiprows=2, header=None, sep=' ', usecols=[0, 1, 2, 3], names=['atom', 'x', 'y', 'z'])
path = '../input/structures/'
filename = 'dsgdb9nsd_000001.xyz'
read_xyz(path, filename)
x_list = []
y_list = []
z_list = []
for filename in os.listdir("../input/structures"):
x_list = x_list + list(read_xyz(path, filename)['x'])
y_list = y_list + list(read_xyz(path, filename)['y'])
z_list = z_list + list(read_xyz(path, filename)['z'])
dimfig, dimaxes = plt.subplots(3, 1, figsize = (6, 6))
sns.distplot(x_list, ax=dimaxes[0])
sns.distplot(y_list, ax=dimaxes[1])
sns.distplot(z_list, ax=dimaxes[2])
print("x max: " + str(np.max(x_list)) + " x min : " + str(np.min(x_list)))
print("y max: " + str(np.max(y_list)) + " y min : " + str(np.min(y_list)))
print("z max: " + str(np.max(z_list)) + " z min : " + str(np.min(z_list)))
coupling_types = set(train['type'])
coupling_types = list(coupling_types)
totals = [np.sum(train['type'] == x) for x in coupling_types]
subsets = dict()
for x in coupling_types:
subsets[x] = train.loc[train['type'] == x]
bar_fig, bar_axis = plt.subplots()
sns.barplot(coupling_types, totals, ax=bar_axis)
dist_fig, dist_axes = plt.subplots(len(subsets), 1, figsize=(6, 12))
for x, y in zip(dist_axes, coupling_types):
sns.distplot(subsets[y]['scalar_coupling_constant'], ax=x)
x.set_title(y)
dist_fig.tight_layout() | code |
105187012/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
X_train | code |
105187012/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
data.head(10) | code |
105187012/cell_25 | [
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
ct1 = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1, 2, 6, 8, 10])], remainder='passthrough')
X = ct1.fit_transform(X)
X = pd.DataFrame(X)
X.plot()
plt.show() | code |
105187012/cell_57 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn import linear_model
Lasso_reg = linear_model.Lasso(alpha=50, max_iter=100, tol=0.1)
Lasso_reg.fit(X_train, y_train) | code |
105187012/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
ct1 = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1, 2, 6, 8, 10])], remainder='passthrough')
X = ct1.fit_transform(X)
X = pd.DataFrame(X)
model = LogisticRegression()
rfe = RFE(model, n_features_to_select=5)
fit = rfe.fit(X, y)
fit.n_features_
features = fit.transform(X)
d = pd.DataFrame(features)
d.hist(figsize=(10, 10))
plt.show() | code |
105187012/cell_30 | [
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
ct1 = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1, 2, 6, 8, 10])], remainder='passthrough')
X = ct1.fit_transform(X)
X = pd.DataFrame(X)
model = LogisticRegression()
rfe = RFE(model, n_features_to_select=5)
fit = rfe.fit(X, y)
fit.n_features_ | code |
105187012/cell_44 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier.fit(X_train, y_train) | code |
105187012/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape | code |
105187012/cell_29 | [
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
ct1 = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1, 2, 6, 8, 10])], remainder='passthrough')
X = ct1.fit_transform(X)
X = pd.DataFrame(X)
model = LogisticRegression()
rfe = RFE(model, n_features_to_select=5)
fit = rfe.fit(X, y)
fit | code |
105187012/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_test = np.array(y_test)
y_pred = np.array(y_pred)
print('DecisionTreeClassifierModel Train Score is : ', classifier.score(X_train, y_train))
print('DecisionTreeClassifierModel Test Score is : ', classifier.score(X_test, y_test)) | code |
105187012/cell_54 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_test = np.array(y_test)
y_pred = np.array(y_pred)
from sklearn.neighbors import KNeighborsClassifier
kNN = KNeighborsClassifier(n_neighbors=20)
kNN.fit(X_train, y_train)
print(kNN.score(X_train, y_train))
print(kNN.score(X_test, y_test)) | code |
105187012/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape | code |
105187012/cell_50 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
ct1 = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1, 2, 6, 8, 10])], remainder='passthrough')
X = ct1.fit_transform(X)
X = pd.DataFrame(X)
model = LogisticRegression()
rfe = RFE(model, n_features_to_select=5)
fit = rfe.fit(X, y)
fit.n_features_
features = fit.transform(X)
d = pd.DataFrame(features)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_test = np.array(y_test)
y_pred = np.array(y_pred)
import matplotlib.pyplot as plt
importance = classifier.feature_importances_
for i, v in enumerate(importance):
print('Feature: %0d, Score: %.5f' % (i, v))
plt.bar([x for x in range(len(importance))], importance)
plt.show() | code |
105187012/cell_52 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_test = np.array(y_test)
y_pred = np.array(y_pred)
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import mean_absolute_error
forest_model = RandomForestClassifier(n_estimators=1000, max_depth=25)
forest_model.fit(X_train, y_train)
y_pred = forest_model.predict(X_test)
print('RandomForestRegressor Train Score is : ', forest_model.score(X_train, y_train))
print('RandomForestRegressor Test Score is : ', forest_model.score(X_test, y_test)) | code |
105187012/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.info() | code |
105187012/cell_49 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, precision_score, f1_score
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_test = np.array(y_test)
y_pred = np.array(y_pred)
recall_score(y_test, y_pred) | code |
105187012/cell_32 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
ct1 = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1, 2, 6, 8, 10])], remainder='passthrough')
X = ct1.fit_transform(X)
X = pd.DataFrame(X)
model = LogisticRegression()
rfe = RFE(model, n_features_to_select=5)
fit = rfe.fit(X, y)
fit.n_features_
features = fit.transform(X)
print(features[0:5, :]) | code |
105187012/cell_59 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_test = np.array(y_test)
y_pred = np.array(y_pred)
from sklearn import linear_model
Lasso_reg = linear_model.Lasso(alpha=50, max_iter=100, tol=0.1)
Lasso_reg.fit(X_train, y_train)
Lasso_reg.score(X_train, y_train)
Lasso_reg.score(X_test, y_test) | code |
105187012/cell_58 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn import linear_model
Lasso_reg = linear_model.Lasso(alpha=50, max_iter=100, tol=0.1)
Lasso_reg.fit(X_train, y_train)
Lasso_reg.score(X_train, y_train) | code |
105187012/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
ct1 = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1, 2, 6, 8, 10])], remainder='passthrough')
X = ct1.fit_transform(X)
X = pd.DataFrame(X)
model = LogisticRegression()
rfe = RFE(model, n_features_to_select=5)
fit = rfe.fit(X, y)
print('Num Features: %s' % fit.n_features_)
print('Selected Features: %s' % fit.support_)
print('Feature Ranking: %s' % fit.ranking_) | code |
105187012/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique() | code |
105187012/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
data.plot()
plt.show() | code |
105187012/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
data['HeartDisease'].value_counts().plot(kind='bar') | code |
105187012/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
ct1 = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1, 2, 6, 8, 10])], remainder='passthrough')
X = ct1.fit_transform(X)
X = pd.DataFrame(X)
model = LogisticRegression()
rfe = RFE(model, n_features_to_select=5)
fit = rfe.fit(X, y)
fit.n_features_
features = fit.transform(X)
d = pd.DataFrame(features)
X_train_d = pd.DataFrame(X_train)
X_train_d.head() | code |
105187012/cell_47 | [
"text_html_output_1.png"
] | from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, precision_score, f1_score
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_test = np.array(y_test)
y_pred = np.array(y_pred)
from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, precision_score, f1_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test, y_pred) | code |
105187012/cell_24 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
ct1 = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1, 2, 6, 8, 10])], remainder='passthrough')
X = ct1.fit_transform(X)
X = pd.DataFrame(X)
X.hist(figsize=(15, 15))
plt.show() | code |
105187012/cell_53 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_test = np.array(y_test)
y_pred = np.array(y_pred)
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, y_train)
print(lr.score(X_train, y_train))
print(lr.score(X_test, y_test)) | code |
105187012/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum() | code |
73081571/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
calory_intake = [2385, 1543, 1293, 2019, 4201, 1203, 2309]
weekly_calory_count = pd.DataFrame({'Days': days_of_week, 'Calories': calory_intake})
import pandas as pd
import matplotlib.pyplot as plt
ser = pd.Series([71, 67, 78, 90], index=('Q1', 'Q2', 'Q3', 'Q4'))
import pandas as pd
import matplotlib.pyplot as plt
ser = pd.Series([77, 65, 34, 93], index=('Math', 'Science', 'English', 'Business'))
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'Group': ['A', 'B', 'C', 'D', 'E'], 'Value': [4, 2, 5, 10, 9]})
plt.barh(y=df.Group, width=df.Value)
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'Group': ['Students', 'Teachers', 'Supervisors', 'Employees', 'Assistants'], 'Value': [2000, 300, 54, 450, 23]})
import pandas as pd
value = pd.DataFrame({'Length': [3.4, 6.39, 3.2, 6.5, 1.3], 'Width': [7.6, 3.6, 0.45, 23.5, 3.2]})
hist = value.hist(bins=5) | code |
73081571/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
calory_intake = [2385, 1543, 1293, 2019, 4201, 1203, 2309]
weekly_calory_count = pd.DataFrame({'Days': days_of_week, 'Calories': calory_intake})
import pandas as pd
import matplotlib.pyplot as plt
ser = pd.Series([71, 67, 78, 90], index=('Q1', 'Q2', 'Q3', 'Q4'))
import pandas as pd
import matplotlib.pyplot as plt
ser = pd.Series([77, 65, 34, 93], index=('Math', 'Science', 'English', 'Business'))
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'Group': ['A', 'B', 'C', 'D', 'E'], 'Value': [4, 2, 5, 10, 9]})
plt.barh(y=df.Group, width=df.Value)
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'Group': ['Students', 'Teachers', 'Supervisors', 'Employees', 'Assistants'], 'Value': [2000, 300, 54, 450, 23]})
import pandas as pd
# Creating a Data frame
value = pd.DataFrame({
'Length': [3.40, 6.39, 3.20, 6.50, 1.3],
'Width': [7.6, 3.6, 0.45, 23.5, 3.2]
})
# Creating Histograms of columns 'Length' and 'Width' using the pandas histogram function : .hist()
# function
hist = value.hist(bins=5)
import pandas as pd
values = pd.DataFrame({'Length': [2.7, 8.7, 3.4, 2.4, 1.9, 3.4, 5.6], 'Breadth': [4.24, 2.67, 7.6, 7.1, 4.9, 6.5, 3.4]})
import pandas as pd
nba_champions = pd.Series(index=[2015, 2016, 2017, 2018, 2019, 2021], data=['Golden State Warriors', 'Golden State Warriors', 'Golden State Warriors', 'Toronto Raptors', 'Los Angeles Lakers', 'Milwaukee Bucks'], name='Winners')
print(nba_champions)
nba_champions_counter = nba_champions.value_counts()
print(nba_champions_counter)
nba_champions_counter.plot(kind='pie') | code |
73081571/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
calory_intake = [2385, 1543, 1293, 2019, 4201, 1203, 2309]
weekly_calory_count = pd.DataFrame({'Days': days_of_week, 'Calories': calory_intake})
weekly_calory_count.plot('Days', 'Calories')
print(weekly_calory_count) | code |
73081571/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
calory_intake = [2385, 1543, 1293, 2019, 4201, 1203, 2309]
weekly_calory_count = pd.DataFrame({'Days': days_of_week, 'Calories': calory_intake})
import pandas as pd
import matplotlib.pyplot as plt
ser = pd.Series([71, 67, 78, 90], index=('Q1', 'Q2', 'Q3', 'Q4'))
import pandas as pd
import matplotlib.pyplot as plt
ser = pd.Series([77, 65, 34, 93], index=('Math', 'Science', 'English', 'Business'))
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'Group': ['A', 'B', 'C', 'D', 'E'], 'Value': [4, 2, 5, 10, 9]})
plt.barh(y=df.Group, width=df.Value) | code |
73081571/cell_31 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
calory_intake = [2385, 1543, 1293, 2019, 4201, 1203, 2309]
weekly_calory_count = pd.DataFrame({'Days': days_of_week, 'Calories': calory_intake})
import pandas as pd
import matplotlib.pyplot as plt
ser = pd.Series([71, 67, 78, 90], index=('Q1', 'Q2', 'Q3', 'Q4'))
import pandas as pd
import matplotlib.pyplot as plt
ser = pd.Series([77, 65, 34, 93], index=('Math', 'Science', 'English', 'Business'))
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'Group': ['A', 'B', 'C', 'D', 'E'], 'Value': [4, 2, 5, 10, 9]})
plt.barh(y=df.Group, width=df.Value)
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'Group': ['Students', 'Teachers', 'Supervisors', 'Employees', 'Assistants'], 'Value': [2000, 300, 54, 450, 23]})
import pandas as pd
# Creating a Data frame
value = pd.DataFrame({
'Length': [3.40, 6.39, 3.20, 6.50, 1.3],
'Width': [7.6, 3.6, 0.45, 23.5, 3.2]
})
# Creating Histograms of columns 'Length' and 'Width' using the pandas histogram function : .hist()
# function
hist = value.hist(bins=5)
import pandas as pd
values = pd.DataFrame({'Length': [2.7, 8.7, 3.4, 2.4, 1.9, 3.4, 5.6], 'Breadth': [4.24, 2.67, 7.6, 7.1, 4.9, 6.5, 3.4]})
import pandas as pd
nba_champions = pd.Series(index=[2015, 2016, 2017, 2018, 2019, 2021], data=['Golden State Warriors', 'Golden State Warriors', 'Golden State Warriors', 'Toronto Raptors', 'Los Angeles Lakers', 'Milwaukee Bucks'], name='Winners')
nba_champions_counter = nba_champions.value_counts()
import pandas as pd
wimbledon_winners = pd.Series(index=[2015, 2016, 2017, 2018, 2019], data=['Novak Djokovic', 'Andy Murray', 'Roger Federer', 'Novak Djokovic', 'Novak Djokovic'], name='Winners')
import pandas as pd
data = {'Name': ['Ben', 'Sally', 'Joseph', 'Penny', 'Jackson', 'Elizabeth'], 'Age': [20, 18, 27, 50, 12, 15]}
df = pd.DataFrame(data=data)
df.plot.scatter(x='Name', y='Age', s=100) | code |
73081571/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
calory_intake = [2385, 1543, 1293, 2019, 4201, 1203, 2309]
weekly_calory_count = pd.DataFrame({'Days': days_of_week, 'Calories': calory_intake})
import pandas as pd
import matplotlib.pyplot as plt
ser = pd.Series([71, 67, 78, 90], index=('Q1', 'Q2', 'Q3', 'Q4'))
ser.plot.bar(rot=5, title='Quarterly Sales(in Millions)')
plt.show(block=True) | code |
90138608/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.columns
sns.pairplot(df) | code |
90138608/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.info() | code |
90138608/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.columns
len(df) | code |
90138608/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.head() | code |
90138608/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90138608/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.columns
sns.catplot(x='Happiness levels(Country)', y='City', kind='bar', data=df.nlargest(10, 'Happiness levels(Country)')) | code |
90138608/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.columns
sns.kdeplot(x='Life expectancy(years) (Country)', data=df, shade=True) | code |
90138608/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.tail() | code |
90138608/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.columns | code |
72088106/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
def getFiles():
""" Dictonary to get the right Files"""
dict = {}
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
dict[filename[0:filename.find('.')]] = pd.read_csv(os.path.join(dirname, filename))
return dict
def preprocessing():
""" Provide the training data for the model. Define the features (X) and the target(y) 'target'."""
dict = getFiles()
train_data = dict['train']
y = train_data.target
train_features = train_data.columns[1:-1]
X = train_data[train_features]
return (X, y)
train_data.head() | code |
72088106/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 |
72088106/cell_5 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
def getFiles():
""" Dictonary to get the right Files"""
dict = {}
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
dict[filename[0:filename.find('.')]] = pd.read_csv(os.path.join(dirname, filename))
return dict
def preprocessing():
""" Provide the training data for the model. Define the features (X) and the target(y) 'target'."""
dict = getFiles()
train_data = dict['train']
y = train_data.target
train_features = train_data.columns[1:-1]
X = train_data[train_features]
return (X, y)
X.head() | code |
2035143/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn import preprocessing, cross_validation, neighbors
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
from sklearn import tree
import graphviz
from sklearn.model_selection import cross_val_score
df = pd.read_csv('../input/glass.csv')
print(df.head()) | code |
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