path
stringlengths 13
17
| screenshot_names
listlengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
122260046/cell_19
|
[
"text_plain_output_1.png"
] |
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
X = train.drop('label', axis=1).values / 255.0
X = X.reshape(-1, 28, 28, 1)
X.shape
Y = train['label'].values
Y.shape
from tensorflow.keras.utils import to_categorical
Y = to_categorical(Y, num_classes=10)
Y.shape
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)
print(f'X_train: {X_train.shape}, y_train: {y_train.shape}')
print('-' * 50)
print(f'X_test: {X_test.shape}, y_test: {y_test.shape}')
|
code
|
122260046/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
|
122260046/cell_7
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
test.info()
|
code
|
122260046/cell_18
|
[
"text_plain_output_1.png"
] |
from tensorflow.keras.utils import to_categorical
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
X = train.drop('label', axis=1).values / 255.0
X = X.reshape(-1, 28, 28, 1)
X.shape
Y = train['label'].values
Y.shape
from tensorflow.keras.utils import to_categorical
Y = to_categorical(Y, num_classes=10)
Y.shape
|
code
|
122260046/cell_28
|
[
"image_output_1.png"
] |
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
def select_pic(i):
arr = np.array(test[i:i + 1])
pix = arr.reshape(arr.shape[0], 28, 28)
img = pix[0]
for i in range(0, 1):
r = np.random.randint(i, 10000)
select_pic(r)
X = train.drop('label', axis=1).values / 255.0
X = X.reshape(-1, 28, 28, 1)
X.shape
Y = train['label'].values
Y.shape
A = test.values / 255.0
A = A.reshape(-1, 28, 28, 1)
A.shape
from tensorflow.keras.utils import to_categorical
Y = to_categorical(Y, num_classes=10)
Y.shape
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)
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=200)
predict = [np.argmax(i) for i in model.predict(A)]
len(predict)
|
code
|
122260046/cell_15
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
X = train.drop('label', axis=1).values / 255.0
X = X.reshape(-1, 28, 28, 1)
X.shape
Y = train['label'].values
Y.shape
|
code
|
122260046/cell_16
|
[
"image_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
A = test.values / 255.0
A = A.reshape(-1, 28, 28, 1)
A.shape
|
code
|
122260046/cell_31
|
[
"text_html_output_1.png"
] |
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
def select_pic(i):
arr = np.array(test[i:i + 1])
pix = arr.reshape(arr.shape[0], 28, 28)
img = pix[0]
for i in range(0, 1):
r = np.random.randint(i, 10000)
select_pic(r)
X = train.drop('label', axis=1).values / 255.0
X = X.reshape(-1, 28, 28, 1)
X.shape
Y = train['label'].values
Y.shape
A = test.values / 255.0
A = A.reshape(-1, 28, 28, 1)
A.shape
from tensorflow.keras.utils import to_categorical
Y = to_categorical(Y, num_classes=10)
Y.shape
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)
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=200)
pd.DataFrame(history.history)
predict = [np.argmax(i) for i in model.predict(A)]
len(predict)
ids = [i + 1 for i in test.index]
len(ids)
submission = pd.DataFrame(columns=['ImageId', 'Label'])
submission['ImageId'] = ids
submission['Label'] = predict
submission
|
code
|
122260046/cell_14
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
X = train.drop('label', axis=1).values / 255.0
X = X.reshape(-1, 28, 28, 1)
X.shape
|
code
|
122260046/cell_22
|
[
"text_plain_output_1.png"
] |
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
from keras.models import Sequential
from tensorflow.keras.utils import plot_model
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
from tensorflow.keras.utils import plot_model
plot_model(model, show_shapes=True, show_layer_names=False, dpi=60, show_layer_activations=True, rankdir='TB')
|
code
|
74070988/cell_11
|
[
"text_html_output_1.png"
] |
from finta import TA
from plotly.subplots import make_subplots
import glob
import numpy as np
import os
import pandas as pd
import plotly.graph_objects as go
import warnings
import os
import glob
import gc
import yaml
import math
import warnings
from tqdm import tqdm
from functools import reduce
import pandas as pd
from finta import TA
from numba import jit
import numpy as np
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from joblib import Parallel, delayed
warnings.simplefilter('ignore')
pd.set_option('max_column', 300)
DATA_PATH = '../input/optiver-realized-volatility-prediction'
__DATA_DIRS__ = ['book_train.parquet', 'trade_tarin.parquet', 'book_test.parquet', 'trade_test.parquet']
FILE_LIST_MAP = {'book_train': glob.glob(os.path.join(DATA_PATH, 'book_train.parquet/*')), 'trade_train': glob.glob(os.path.join(DATA_PATH, 'trade_train.parquet/*')), 'book_test': glob.glob(os.path.join(DATA_PATH, 'book_test.parquet/*')), 'trade_test': glob.glob(os.path.join(DATA_PATH, 'trade_test.parquet/*'))}
ORDER_PRICE = ['bid_price1', 'bid_price2', 'ask_price1', 'ask_price2']
ORDER_VOLUME = ['bid_size1', 'bid_size2', 'ask_size1', 'ask_size2']
TI_NAMES = [func for func in dir(TA) if callable(getattr(TA, func)) and (not func.startswith('_'))]
def get_wap(df_book):
"""Compute estimated price series.
Parameters:
df_book: pd.DataFrame, raw information of book data
Return:
wap: pd.DataFrame, estimated price series with time identifiers
"""
df_book_ = df_book.copy()
df_book_['wap1'] = (df_book_['bid_price1'] * df_book_['ask_size1'] + df_book_['ask_price1'] * df_book_['bid_size1']) / (df_book_['bid_size1'] + df_book_['ask_size1'])
wap = df_book_.loc[:, ['time_id', 'seconds_in_bucket', 'wap1']]
return wap
def get_ohlcv(prices, volumes, scale=10):
"""Return OHLCV of stock price based on the Kline scale (sec).
Parameters:
prices: pd.Series, the estimated price series
volumes: pd.Series, the trading volume series
scale: int, the scale of the Kline (sec), default=10
Return:
OHLCV: pd.DataFrame, four prices and trading volumes of the stock based on the Kline scale
"""
if 600 % scale != 0:
raise ValueError('Choose scale divisible by 600 seconds...')
OHLCV = pd.DataFrame(columns=['open', 'high', 'low', 'close', 'volume'])
for i in range(0, 600, scale):
p_window = prices[i:i + scale]
v_window = volumes[i:i + scale]
p_stick = {'open': p_window.iloc[0], 'high': np.max(p_window), 'low': np.min(p_window), 'close': p_window.iloc[-1], 'volume': np.sum(v_window)}
OHLCV = OHLCV.append(p_stick, ignore_index=True)
OHLCV.insert(0, column='sec', value=[scale * (i + 1) for i in range(0, 600 // scale)])
return OHLCV
def ffill(df):
"""Forward fill information in order book data, followed by bfill to avoid bug in filler data.
"""
df_ = df.copy()
df_.set_index(['time_id', 'seconds_in_bucket'], inplace=True)
df_ = df_.reindex(pd.MultiIndex.from_product([df_.index.levels[0], np.arange(0, 600)], names=['time_id', 'seconds_in_bucket']), method='ffill')
df_ = df_.reindex(pd.MultiIndex.from_product([df_.index.levels[0], np.arange(0, 600)], names=['time_id', 'seconds_in_bucket']), method='bfill')
df_.reset_index(inplace=True)
return df_
df_order = pd.read_parquet(os.path.join(DATA_PATH, 'book_train.parquet/stock_id=0/'))
df_trade = pd.read_parquet(os.path.join(DATA_PATH, 'trade_train.parquet/stock_id=0/'))
df_order = df_order[df_order['time_id'] == 5]
df_order = ffill(df_order)
df_trade = df_trade[df_trade['time_id'] == 5]
wap = get_wap(df_order)
df = wap.merge(df_trade.loc[:, ['seconds_in_bucket', 'size']], on=['seconds_in_bucket'], how='outer')
df.fillna(0, inplace=True)
ohlcv = get_ohlcv(df['wap1'], df['size'], scale=10)
fig = make_subplots(rows=2, cols=1, shared_xaxes=True)
fig.add_trace(go.Candlestick(x=ohlcv['sec'], open=ohlcv['open'], high=ohlcv['high'], low=ohlcv['low'], close=ohlcv['close']), row=1, col=1)
fig.add_trace(go.Bar(x=ohlcv['sec'], y=ohlcv['volume']), row=2, col=1)
fig.update(layout_xaxis_rangeslider_visible=False)
fig.show()
|
code
|
74070988/cell_15
|
[
"text_html_output_2.png"
] |
from finta import TA
from joblib import Parallel, delayed
from plotly.subplots import make_subplots
from tqdm import tqdm
import gc
import glob
import numpy as np
import os
import pandas as pd
import plotly.graph_objects as go
import warnings
import os
import glob
import gc
import yaml
import math
import warnings
from tqdm import tqdm
from functools import reduce
import pandas as pd
from finta import TA
from numba import jit
import numpy as np
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from joblib import Parallel, delayed
warnings.simplefilter('ignore')
pd.set_option('max_column', 300)
DATA_PATH = '../input/optiver-realized-volatility-prediction'
__DATA_DIRS__ = ['book_train.parquet', 'trade_tarin.parquet', 'book_test.parquet', 'trade_test.parquet']
FILE_LIST_MAP = {'book_train': glob.glob(os.path.join(DATA_PATH, 'book_train.parquet/*')), 'trade_train': glob.glob(os.path.join(DATA_PATH, 'trade_train.parquet/*')), 'book_test': glob.glob(os.path.join(DATA_PATH, 'book_test.parquet/*')), 'trade_test': glob.glob(os.path.join(DATA_PATH, 'trade_test.parquet/*'))}
ORDER_PRICE = ['bid_price1', 'bid_price2', 'ask_price1', 'ask_price2']
ORDER_VOLUME = ['bid_size1', 'bid_size2', 'ask_size1', 'ask_size2']
TI_NAMES = [func for func in dir(TA) if callable(getattr(TA, func)) and (not func.startswith('_'))]
def get_wap(df_book):
"""Compute estimated price series.
Parameters:
df_book: pd.DataFrame, raw information of book data
Return:
wap: pd.DataFrame, estimated price series with time identifiers
"""
df_book_ = df_book.copy()
df_book_['wap1'] = (df_book_['bid_price1'] * df_book_['ask_size1'] + df_book_['ask_price1'] * df_book_['bid_size1']) / (df_book_['bid_size1'] + df_book_['ask_size1'])
wap = df_book_.loc[:, ['time_id', 'seconds_in_bucket', 'wap1']]
return wap
def get_ohlcv(prices, volumes, scale=10):
"""Return OHLCV of stock price based on the Kline scale (sec).
Parameters:
prices: pd.Series, the estimated price series
volumes: pd.Series, the trading volume series
scale: int, the scale of the Kline (sec), default=10
Return:
OHLCV: pd.DataFrame, four prices and trading volumes of the stock based on the Kline scale
"""
if 600 % scale != 0:
raise ValueError('Choose scale divisible by 600 seconds...')
OHLCV = pd.DataFrame(columns=['open', 'high', 'low', 'close', 'volume'])
for i in range(0, 600, scale):
p_window = prices[i:i + scale]
v_window = volumes[i:i + scale]
p_stick = {'open': p_window.iloc[0], 'high': np.max(p_window), 'low': np.min(p_window), 'close': p_window.iloc[-1], 'volume': np.sum(v_window)}
OHLCV = OHLCV.append(p_stick, ignore_index=True)
OHLCV.insert(0, column='sec', value=[scale * (i + 1) for i in range(0, 600 // scale)])
return OHLCV
def ffill(df):
"""Forward fill information in order book data, followed by bfill to avoid bug in filler data.
"""
df_ = df.copy()
df_.set_index(['time_id', 'seconds_in_bucket'], inplace=True)
df_ = df_.reindex(pd.MultiIndex.from_product([df_.index.levels[0], np.arange(0, 600)], names=['time_id', 'seconds_in_bucket']), method='ffill')
df_ = df_.reindex(pd.MultiIndex.from_product([df_.index.levels[0], np.arange(0, 600)], names=['time_id', 'seconds_in_bucket']), method='bfill')
df_.reset_index(inplace=True)
return df_
df_order = pd.read_parquet(os.path.join(DATA_PATH, 'book_train.parquet/stock_id=0/'))
df_trade = pd.read_parquet(os.path.join(DATA_PATH, 'trade_train.parquet/stock_id=0/'))
df_order = df_order[df_order['time_id'] == 5]
df_order = ffill(df_order)
df_trade = df_trade[df_trade['time_id'] == 5]
wap = get_wap(df_order)
df = wap.merge(df_trade.loc[:, ['seconds_in_bucket', 'size']], on=['seconds_in_bucket'], how='outer')
df.fillna(0, inplace=True)
ohlcv = get_ohlcv(df['wap1'], df['size'], scale=10)
fig = make_subplots(rows=2, cols=1, shared_xaxes=True)
fig.add_trace(go.Candlestick(
x=ohlcv['sec'],
open=ohlcv['open'],
high=ohlcv['high'],
low=ohlcv['low'],
close=ohlcv['close']),
row=1, col=1
)
fig.add_trace(go.Bar(
x=ohlcv['sec'],
y=ohlcv['volume']),
row=2, col=1
)
fig.update(layout_xaxis_rangeslider_visible=False)
fig.show()
def get_dataset(datatype, n_jobs, scale):
"""Return processed dataset after running parallel dataset generation.
Parameters:
datatype: str, dataset type, the choices are as follows:
{'train', 'test'}
n_jobs: int, num of processors to work
scale: int, the scale of the Kline (sec)
Return:
df_proc: pd.DataFrame, dataset containing derived technical indicators
"""
df_proc = Parallel(n_jobs=n_jobs)((delayed(gen_dataset)(book_file, trade_file, scale) for book_file, trade_file in tqdm(zip(sorted(FILE_LIST_MAP[f'book_{datatype}']), sorted(FILE_LIST_MAP[f'trade_{datatype}'])))))
df_proc = pd.concat(df_proc, ignore_index=True)
return df_proc
def gen_dataset(book_file, trade_file, scale):
"""Generate dataset for one stock.
Parameters:
book_file: str, file path of the book data
trade_file: str, file path of the trade data
scale: int, the scale of the Kline (sec)
"""
assert book_file.split('=')[1] == trade_file.split('=')[1]
stock_id = book_file.split('=')[1]
df_book = pd.read_parquet(book_file)
df_book = ffill(df_book)
df_trade = pd.read_parquet(trade_file)
df = get_wap(df_book)
df = df.merge(df_trade.loc[:, ['time_id', 'seconds_in_bucket', 'size']], on=['time_id', 'seconds_in_bucket'], how='outer')
df.fillna(0, inplace=True)
del df_book, df_trade
_ = gc.collect()
tis = get_tis(df, scale)
stats = {col: ['mean', 'median', 'min', 'max', 'std'] for col in tis.columns if col != 'time_id'}
df_stats = cal_stats(tis, stats)
df_stats['row_id'] = df_stats['time_id'].apply(lambda t: f'{stock_id}-{t}')
df_stats['stock_id'] = int(stock_id)
return df_stats
def get_tis(df, scale):
"""Compute all technical indicators provided by finta.
Parameters:
df: pd.DataFrame, book data merged with trade data
scale: int, the scale of the Kline (sec)
"""
tis = pd.DataFrame()
for time_id, gp in df.groupby('time_id'):
ohlcv_ = get_ohlcv(gp['wap1'], gp['size'], scale=scale)
ohlcv_.set_index(pd.DatetimeIndex(ohlcv_['sec']), inplace=True)
ohlcv_.drop('sec', axis=1, inplace=True)
for ti_name, ti in TIS.items():
result = ti(ohlcv_)
if type(result) == pd.core.series.Series:
result.name = ti_name
else:
result.columns = [f'{ti_name}_{col}' for col in result.columns]
ohlcv_ = ohlcv_.merge(result, right_index=True, left_index=True)
ohlcv_['time_id'] = int(time_id)
tis = pd.concat([tis, ohlcv_], ignore_index=True)
return tis
def cal_stats(df, ft_stats):
"""Calculate specified stats for given dataframe.
Parameters:
df: pd.DataFrame, dataframe containing raw features
ft_stats: dict[str, list], mapping relationship between features and the stats (e.g., mean, median, min, max, std)
Return:
df_stats: pd.DataFrame, dataframe containing derived stats
"""
df_ = df.groupby(by=['time_id'])
df_stats = df_.agg(ft_stats)
df_stats.columns = ['_'.join(sub_str) for sub_str in df_stats.columns]
df_stats.reset_index(inplace=True)
return df_stats
TIS = {'ADL': getattr(TA, 'ADL')}
TIS
|
code
|
74070988/cell_10
|
[
"text_plain_output_1.png"
] |
from finta import TA
import glob
import numpy as np
import os
import pandas as pd
import warnings
import os
import glob
import gc
import yaml
import math
import warnings
from tqdm import tqdm
from functools import reduce
import pandas as pd
from finta import TA
from numba import jit
import numpy as np
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from joblib import Parallel, delayed
warnings.simplefilter('ignore')
pd.set_option('max_column', 300)
DATA_PATH = '../input/optiver-realized-volatility-prediction'
__DATA_DIRS__ = ['book_train.parquet', 'trade_tarin.parquet', 'book_test.parquet', 'trade_test.parquet']
FILE_LIST_MAP = {'book_train': glob.glob(os.path.join(DATA_PATH, 'book_train.parquet/*')), 'trade_train': glob.glob(os.path.join(DATA_PATH, 'trade_train.parquet/*')), 'book_test': glob.glob(os.path.join(DATA_PATH, 'book_test.parquet/*')), 'trade_test': glob.glob(os.path.join(DATA_PATH, 'trade_test.parquet/*'))}
ORDER_PRICE = ['bid_price1', 'bid_price2', 'ask_price1', 'ask_price2']
ORDER_VOLUME = ['bid_size1', 'bid_size2', 'ask_size1', 'ask_size2']
TI_NAMES = [func for func in dir(TA) if callable(getattr(TA, func)) and (not func.startswith('_'))]
def get_wap(df_book):
"""Compute estimated price series.
Parameters:
df_book: pd.DataFrame, raw information of book data
Return:
wap: pd.DataFrame, estimated price series with time identifiers
"""
df_book_ = df_book.copy()
df_book_['wap1'] = (df_book_['bid_price1'] * df_book_['ask_size1'] + df_book_['ask_price1'] * df_book_['bid_size1']) / (df_book_['bid_size1'] + df_book_['ask_size1'])
wap = df_book_.loc[:, ['time_id', 'seconds_in_bucket', 'wap1']]
return wap
def get_ohlcv(prices, volumes, scale=10):
"""Return OHLCV of stock price based on the Kline scale (sec).
Parameters:
prices: pd.Series, the estimated price series
volumes: pd.Series, the trading volume series
scale: int, the scale of the Kline (sec), default=10
Return:
OHLCV: pd.DataFrame, four prices and trading volumes of the stock based on the Kline scale
"""
if 600 % scale != 0:
raise ValueError('Choose scale divisible by 600 seconds...')
OHLCV = pd.DataFrame(columns=['open', 'high', 'low', 'close', 'volume'])
for i in range(0, 600, scale):
p_window = prices[i:i + scale]
v_window = volumes[i:i + scale]
p_stick = {'open': p_window.iloc[0], 'high': np.max(p_window), 'low': np.min(p_window), 'close': p_window.iloc[-1], 'volume': np.sum(v_window)}
OHLCV = OHLCV.append(p_stick, ignore_index=True)
OHLCV.insert(0, column='sec', value=[scale * (i + 1) for i in range(0, 600 // scale)])
return OHLCV
def ffill(df):
"""Forward fill information in order book data, followed by bfill to avoid bug in filler data.
"""
df_ = df.copy()
df_.set_index(['time_id', 'seconds_in_bucket'], inplace=True)
df_ = df_.reindex(pd.MultiIndex.from_product([df_.index.levels[0], np.arange(0, 600)], names=['time_id', 'seconds_in_bucket']), method='ffill')
df_ = df_.reindex(pd.MultiIndex.from_product([df_.index.levels[0], np.arange(0, 600)], names=['time_id', 'seconds_in_bucket']), method='bfill')
df_.reset_index(inplace=True)
return df_
df_order = pd.read_parquet(os.path.join(DATA_PATH, 'book_train.parquet/stock_id=0/'))
df_trade = pd.read_parquet(os.path.join(DATA_PATH, 'trade_train.parquet/stock_id=0/'))
df_order = df_order[df_order['time_id'] == 5]
df_order = ffill(df_order)
df_trade = df_trade[df_trade['time_id'] == 5]
df_order.head()
|
code
|
74070988/cell_5
|
[
"text_plain_output_1.png"
] |
!pip install finta --no-index --find-links=file:///kaggle/input/fin-ta/finta
|
code
|
88086228/cell_21
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.countplot(x='Survived', hue='Sex', data=train)
|
code
|
88086228/cell_13
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.shape
|
code
|
88086228/cell_9
|
[
"text_html_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.head(2)
|
code
|
88086228/cell_25
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.boxplot(x='Survived', y='Fare', data=train)
|
code
|
88086228/cell_57
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
def train_age(cols):
Age = cols[0]
Pclass = cols[1]
if pd.isnull(Age):
if Pclass == 1:
return 37
if Pclass == 2:
return 29
else:
return 24
else:
return Age
def test_age(cols):
Age = cols[0]
Pclass = cols[1]
if pd.isnull(Age):
if Pclass == 1:
return 42
if Pclass == 2:
return 26.5
else:
return 24
else:
return Age
train = train[train['Fare'] < 300].reset_index(drop=True)
sex_train = pd.get_dummies(train['Sex'], drop_first=True)
sex_test = pd.get_dummies(test['Sex'], drop_first=True)
embark_train = pd.get_dummies(train['Embarked'], drop_first=True)
embark_test = pd.get_dummies(test['Embarked'], drop_first=True)
train = pd.concat([train, sex_train, embark_train], axis=1)
test = pd.concat([test, sex_test, embark_test], axis=1)
test.head(2)
|
code
|
88086228/cell_56
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
def train_age(cols):
Age = cols[0]
Pclass = cols[1]
if pd.isnull(Age):
if Pclass == 1:
return 37
if Pclass == 2:
return 29
else:
return 24
else:
return Age
def test_age(cols):
Age = cols[0]
Pclass = cols[1]
if pd.isnull(Age):
if Pclass == 1:
return 42
if Pclass == 2:
return 26.5
else:
return 24
else:
return Age
train = train[train['Fare'] < 300].reset_index(drop=True)
sex_train = pd.get_dummies(train['Sex'], drop_first=True)
sex_test = pd.get_dummies(test['Sex'], drop_first=True)
embark_train = pd.get_dummies(train['Embarked'], drop_first=True)
embark_test = pd.get_dummies(test['Embarked'], drop_first=True)
train = pd.concat([train, sex_train, embark_train], axis=1)
test = pd.concat([test, sex_test, embark_test], axis=1)
train.head(2)
|
code
|
88086228/cell_34
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
sns.boxplot(x='Pclass', y='Age', data=train)
print('Median age of each class')
print('Pclass 1:', train['Age'][train['Pclass'] == 1].median())
print('Pclass 2:', train['Age'][train['Pclass'] == 2].median())
print('Pclass 3:', train['Age'][train['Pclass'] == 3].median())
|
code
|
88086228/cell_23
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.countplot(x='Survived', hue='SibSp', data=train)
plt.legend(loc='upper right', title='SibSp')
|
code
|
88086228/cell_30
|
[
"image_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
print('TRAIN DATASET')
print(train.isnull().sum() / len(train) * 100)
print('=' * 40)
print('TEST DATASET')
print(test.isnull().sum() / len(test) * 100)
|
code
|
88086228/cell_20
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.countplot(x='Survived', data=train)
|
code
|
88086228/cell_6
|
[
"text_html_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
|
code
|
88086228/cell_40
|
[
"image_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
print(train['Embarked'].value_counts())
|
code
|
88086228/cell_26
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.boxplot(x='Survived', y='Age', data=train)
|
code
|
88086228/cell_50
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
train[train['Fare'] > 300]
|
code
|
88086228/cell_7
|
[
"text_html_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.info()
|
code
|
88086228/cell_49
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
sns.displot(x='Fare', data=train)
|
code
|
88086228/cell_8
|
[
"text_html_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.describe().transpose()
|
code
|
88086228/cell_15
|
[
"text_html_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.shape
test.describe().transpose()
|
code
|
88086228/cell_16
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.shape
test.head(2)
|
code
|
88086228/cell_17
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
|
code
|
88086228/cell_35
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
sns.boxplot(x='Pclass', y='Age', data=test)
print('Median age of each class')
print('Pclass 1:', test['Age'][test['Pclass'] == 1].median())
print('Pclass 2:', test['Age'][test['Pclass'] == 2].median())
print('Pclass 3:', test['Age'][test['Pclass'] == 3].median())
|
code
|
88086228/cell_43
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
test['Fare'].median()
|
code
|
88086228/cell_46
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
test.shape
train.nunique()
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
print('TRAIN DATASET')
print(train.isnull().sum() / len(train) * 100)
print('=' * 40)
print('TEST DATASET')
print(test.isnull().sum() / len(test) * 100)
|
code
|
88086228/cell_24
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.countplot(x='Survived', hue='Parch', data=train)
|
code
|
88086228/cell_14
|
[
"text_html_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.shape
test.info()
|
code
|
88086228/cell_22
|
[
"text_html_output_1.png"
] |
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
train.nunique()
sns.countplot(x='Survived', hue='Pclass', data=train)
|
code
|
88086228/cell_10
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.shape
train.nunique()
|
code
|
1006755/cell_42
|
[
"text_html_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
def find_one(substrs, superstr):
for substr in substrs:
if superstr.find(substr) != -1:
return substr
return ''
address_values = df['ADDRESS'].values
street_values = []
for name_value in address_values:
street_values.append(find_one(['N PULASKI', 'W PERSHING', 'S WESTERN', 'W JACKSON', 'W MADISON', 'N HUMBOLDT', 'W AUGUSTA', 'S COTTAGE GROVE', 'S KEDZIE', 'W 71ST', 'W ROOSEVELT', 'W OGDEN', 'W PETERSON', 'W FOSTER', 'N WESTERN', 'W ADDISON', 'E MORGAN DR', 'S PULASKI', 'S ARCHER', 'S STATE', 'E 95TH', 'W FULLERTON', 'W GRAND', 'W 127TH', 'W 111TH', 'N CENTRAL AVE', 'W IRVING PARK', 'N LINCOLN', 'S CENTRAL AVE', 'S VINCENNES', 'W 79TH', 'N ASHLAND', 'N OGDEN', 'W BELMONT AVE', 'N MILWAUKEE AVE', 'N CLYBOURN AVE', 'N RIDGE AVE', 'N BROADWAY', 'W 51ST ST', 'S JEFFERY', 'W HIGGINS', 'W LAWRENCE', 'N NARRAGANSETT AVE', 'W CHICAGO AVE', 'S HALSTED', 'S RACINE AVE', 'W GARFIELD BLVD', 'S INDIANAPOLIS', 'N COLUMBUS DR', 'W 76th ST', 'E 75TH ST', 'W 55TH', 'W MONTROSE', 'E ILLINOIS ST', 'S EWING AVE', 'W SUPERIOR ST', 'E 95TH ST', 'W CERMAK RD', 'N CICERO AVE', 'W DIVISION ST', 'W BRYN MAWR AVE', 'N NORTHWEST HWY', 'E 87TH ST', 'E 63RD ST', 'S MARTIN LUTHER KING', 'S ASHLAND AVE', 'W 83rd ST', 'W 103RD ST', 'W NORTH AVE'], name_value))
one_hot = pd.get_dummies(street_values, 'Title', '_')
df.drop('ADDRESS', axis=1, inplace=True)
df = pd.concat([df, one_hot], axis=1)
df.columns
df.drop('Title_', axis=1, inplace=True)
df.columns
|
code
|
1006755/cell_13
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
|
code
|
1006755/cell_9
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
|
code
|
1006755/cell_25
|
[
"text_html_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
|
code
|
1006755/cell_34
|
[
"text_html_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
df[df['ADDRESS'] == '2912 W ROOSEVELT']
|
code
|
1006755/cell_44
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
def find_one(substrs, superstr):
for substr in substrs:
if superstr.find(substr) != -1:
return substr
return ''
address_values = df['ADDRESS'].values
street_values = []
for name_value in address_values:
street_values.append(find_one(['N PULASKI', 'W PERSHING', 'S WESTERN', 'W JACKSON', 'W MADISON', 'N HUMBOLDT', 'W AUGUSTA', 'S COTTAGE GROVE', 'S KEDZIE', 'W 71ST', 'W ROOSEVELT', 'W OGDEN', 'W PETERSON', 'W FOSTER', 'N WESTERN', 'W ADDISON', 'E MORGAN DR', 'S PULASKI', 'S ARCHER', 'S STATE', 'E 95TH', 'W FULLERTON', 'W GRAND', 'W 127TH', 'W 111TH', 'N CENTRAL AVE', 'W IRVING PARK', 'N LINCOLN', 'S CENTRAL AVE', 'S VINCENNES', 'W 79TH', 'N ASHLAND', 'N OGDEN', 'W BELMONT AVE', 'N MILWAUKEE AVE', 'N CLYBOURN AVE', 'N RIDGE AVE', 'N BROADWAY', 'W 51ST ST', 'S JEFFERY', 'W HIGGINS', 'W LAWRENCE', 'N NARRAGANSETT AVE', 'W CHICAGO AVE', 'S HALSTED', 'S RACINE AVE', 'W GARFIELD BLVD', 'S INDIANAPOLIS', 'N COLUMBUS DR', 'W 76th ST', 'E 75TH ST', 'W 55TH', 'W MONTROSE', 'E ILLINOIS ST', 'S EWING AVE', 'W SUPERIOR ST', 'E 95TH ST', 'W CERMAK RD', 'N CICERO AVE', 'W DIVISION ST', 'W BRYN MAWR AVE', 'N NORTHWEST HWY', 'E 87TH ST', 'E 63RD ST', 'S MARTIN LUTHER KING', 'S ASHLAND AVE', 'W 83rd ST', 'W 103RD ST', 'W NORTH AVE'], name_value))
one_hot = pd.get_dummies(street_values, 'Title', '_')
df.drop('ADDRESS', axis=1, inplace=True)
df = pd.concat([df, one_hot], axis=1)
df.columns
df.drop('Title_', axis=1, inplace=True)
df.columns
date_group = df.groupby(['DATE'])
fig, plot = plt.subplots(figsize=[20, 7])
date_group['VIOLATIONS'].sum().sort_index().plot(color='green')
plot.set_title('Violations on different days', fontsize=25)
plot.tick_params(labelsize='large')
plot.set_ylabel('No of violations', fontsize=20)
plot.set_xlabel('Dates', fontsize=20)
|
code
|
1006755/cell_20
|
[
"text_html_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.info()
|
code
|
1006755/cell_29
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
|
code
|
1006755/cell_39
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
def find_one(substrs, superstr):
for substr in substrs:
if superstr.find(substr) != -1:
return substr
return ''
address_values = df['ADDRESS'].values
street_values = []
for name_value in address_values:
street_values.append(find_one(['N PULASKI', 'W PERSHING', 'S WESTERN', 'W JACKSON', 'W MADISON', 'N HUMBOLDT', 'W AUGUSTA', 'S COTTAGE GROVE', 'S KEDZIE', 'W 71ST', 'W ROOSEVELT', 'W OGDEN', 'W PETERSON', 'W FOSTER', 'N WESTERN', 'W ADDISON', 'E MORGAN DR', 'S PULASKI', 'S ARCHER', 'S STATE', 'E 95TH', 'W FULLERTON', 'W GRAND', 'W 127TH', 'W 111TH', 'N CENTRAL AVE', 'W IRVING PARK', 'N LINCOLN', 'S CENTRAL AVE', 'S VINCENNES', 'W 79TH', 'N ASHLAND', 'N OGDEN', 'W BELMONT AVE', 'N MILWAUKEE AVE', 'N CLYBOURN AVE', 'N RIDGE AVE', 'N BROADWAY', 'W 51ST ST', 'S JEFFERY', 'W HIGGINS', 'W LAWRENCE', 'N NARRAGANSETT AVE', 'W CHICAGO AVE', 'S HALSTED', 'S RACINE AVE', 'W GARFIELD BLVD', 'S INDIANAPOLIS', 'N COLUMBUS DR', 'W 76th ST', 'E 75TH ST', 'W 55TH', 'W MONTROSE', 'E ILLINOIS ST', 'S EWING AVE', 'W SUPERIOR ST', 'E 95TH ST', 'W CERMAK RD', 'N CICERO AVE', 'W DIVISION ST', 'W BRYN MAWR AVE', 'N NORTHWEST HWY', 'E 87TH ST', 'E 63RD ST', 'S MARTIN LUTHER KING', 'S ASHLAND AVE', 'W 83rd ST', 'W 103RD ST', 'W NORTH AVE'], name_value))
one_hot = pd.get_dummies(street_values, 'Title', '_')
df.drop('ADDRESS', axis=1, inplace=True)
df = pd.concat([df, one_hot], axis=1)
df.columns
df[990:1000]
|
code
|
1006755/cell_11
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
|
code
|
1006755/cell_19
|
[
"text_html_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.describe(include='all')
|
code
|
1006755/cell_32
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
df[5:15]
|
code
|
1006755/cell_28
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
|
code
|
1006755/cell_15
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.head()
|
code
|
1006755/cell_16
|
[
"text_html_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.tail()
|
code
|
1006755/cell_38
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
def find_one(substrs, superstr):
for substr in substrs:
if superstr.find(substr) != -1:
return substr
return ''
address_values = df['ADDRESS'].values
street_values = []
for name_value in address_values:
street_values.append(find_one(['N PULASKI', 'W PERSHING', 'S WESTERN', 'W JACKSON', 'W MADISON', 'N HUMBOLDT', 'W AUGUSTA', 'S COTTAGE GROVE', 'S KEDZIE', 'W 71ST', 'W ROOSEVELT', 'W OGDEN', 'W PETERSON', 'W FOSTER', 'N WESTERN', 'W ADDISON', 'E MORGAN DR', 'S PULASKI', 'S ARCHER', 'S STATE', 'E 95TH', 'W FULLERTON', 'W GRAND', 'W 127TH', 'W 111TH', 'N CENTRAL AVE', 'W IRVING PARK', 'N LINCOLN', 'S CENTRAL AVE', 'S VINCENNES', 'W 79TH', 'N ASHLAND', 'N OGDEN', 'W BELMONT AVE', 'N MILWAUKEE AVE', 'N CLYBOURN AVE', 'N RIDGE AVE', 'N BROADWAY', 'W 51ST ST', 'S JEFFERY', 'W HIGGINS', 'W LAWRENCE', 'N NARRAGANSETT AVE', 'W CHICAGO AVE', 'S HALSTED', 'S RACINE AVE', 'W GARFIELD BLVD', 'S INDIANAPOLIS', 'N COLUMBUS DR', 'W 76th ST', 'E 75TH ST', 'W 55TH', 'W MONTROSE', 'E ILLINOIS ST', 'S EWING AVE', 'W SUPERIOR ST', 'E 95TH ST', 'W CERMAK RD', 'N CICERO AVE', 'W DIVISION ST', 'W BRYN MAWR AVE', 'N NORTHWEST HWY', 'E 87TH ST', 'E 63RD ST', 'S MARTIN LUTHER KING', 'S ASHLAND AVE', 'W 83rd ST', 'W 103RD ST', 'W NORTH AVE'], name_value))
one_hot = pd.get_dummies(street_values, 'Title', '_')
df.drop('ADDRESS', axis=1, inplace=True)
df = pd.concat([df, one_hot], axis=1)
df.columns
|
code
|
1006755/cell_17
|
[
"text_html_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df[5:15]
|
code
|
1006755/cell_31
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
df.info()
|
code
|
1006755/cell_24
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df[df['LATITUDE'] == -1]
|
code
|
1006755/cell_22
|
[
"text_plain_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
|
code
|
1006755/cell_36
|
[
"text_html_output_1.png"
] |
import pandas as pd
df = pd.read_csv('../input/cameras.csv')
df.shape
df.columns.values
df.dtypes
df.replace('NaN', -1, inplace=True)
(df['LATITUDE'] == -1).count()
df.dtypes
df.shape
df.columns
df.drop('LOCATION', axis=1, inplace=True)
df['ADDRESS'].unique()
|
code
|
73095876/cell_6
|
[
"text_plain_output_1.png"
] |
from google.cloud import bigquery
bigquery_client = bigquery.Client(project='wtm-kampala-ds', location='US')
dataset_ref = bigquery_client.dataset('openaq', project='bigquery-public-data')
dataset = bigquery_client.get_dataset(dataset_ref)
[x.table_id for x in bigquery_client.list_tables(dataset)]
|
code
|
72098873/cell_21
|
[
"text_plain_output_1.png"
] |
from sklearn import metrics
from sklearn import svm
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
ax=plt.subplots(1,1,figsize=(10,8))
iris['Species'].value_counts().plot.pie(autopct='%1.1f%%',shadow=True,figsize=(10,8))
plt.title("Iris Species %")
plt.show()
iris.shape
train, test = train_test_split(iris, test_size=0.25)
train_X = train[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
train_y = train.Species
test_X = test[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
test_y = test.Species
model = svm.SVC()
model.fit(train_X, train_y)
prediction = model.predict(test_X)
model = LogisticRegression()
model.fit(train_X, train_y)
prediction = model.predict(test_X)
print('The accuracy of the Logistic Regression is', metrics.accuracy_score(prediction, test_y))
|
code
|
72098873/cell_13
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
iris.shape
|
code
|
72098873/cell_9
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
|
code
|
72098873/cell_4
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris.plot(kind='scatter', x='SepalLengthCm', y='SepalWidthCm')
|
code
|
72098873/cell_20
|
[
"text_plain_output_1.png"
] |
from sklearn import metrics
from sklearn import svm
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
ax=plt.subplots(1,1,figsize=(10,8))
iris['Species'].value_counts().plot.pie(autopct='%1.1f%%',shadow=True,figsize=(10,8))
plt.title("Iris Species %")
plt.show()
iris.shape
train, test = train_test_split(iris, test_size=0.25)
train_X = train[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
train_y = train.Species
test_X = test[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
test_y = test.Species
model = svm.SVC()
model.fit(train_X, train_y)
prediction = model.predict(test_X)
print('The accuracy of the SVM is:', metrics.accuracy_score(prediction, test_y))
|
code
|
72098873/cell_6
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species == 'Iris-setosa'].plot.scatter(x='PetalLengthCm', y='PetalWidthCm', color='orange', label='Setosa')
iris[iris.Species == 'Iris-versicolor'].plot.scatter(x='PetalLengthCm', y='PetalWidthCm', color='blue', label='versicolor', ax=fig)
iris[iris.Species == 'Iris-virginica'].plot.scatter(x='PetalLengthCm', y='PetalWidthCm', color='green', label='virginica', ax=fig)
fig.set_xlabel('Petal Length')
fig.set_ylabel('Petal Width')
fig.set_title(' Petal Length VS Width')
fig = plt.gcf()
fig.set_size_inches(10, 6)
plt.show()
|
code
|
72098873/cell_2
|
[
"image_output_1.png"
] |
import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris.info()
|
code
|
72098873/cell_11
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
iris['Species'].value_counts()
|
code
|
72098873/cell_19
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
ax=plt.subplots(1,1,figsize=(10,8))
iris['Species'].value_counts().plot.pie(autopct='%1.1f%%',shadow=True,figsize=(10,8))
plt.title("Iris Species %")
plt.show()
iris.shape
train, test = train_test_split(iris, test_size=0.25)
train_X = train[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
train_y = train.Species
test_X = test[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
test_y = test.Species
test_y.head(5)
|
code
|
72098873/cell_1
|
[
"text_html_output_1.png"
] |
import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris.head()
|
code
|
72098873/cell_7
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
sns.boxplot(x='Species', y='PetalLengthCm', data=iris)
|
code
|
72098873/cell_18
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
ax=plt.subplots(1,1,figsize=(10,8))
iris['Species'].value_counts().plot.pie(autopct='%1.1f%%',shadow=True,figsize=(10,8))
plt.title("Iris Species %")
plt.show()
iris.shape
train, test = train_test_split(iris, test_size=0.25)
train_X = train[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
train_y = train.Species
test_X = test[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
test_y = test.Species
train_X.head(5)
|
code
|
72098873/cell_8
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
sns.pairplot(iris.drop('Id', axis=1), hue='Species', height=3)
|
code
|
72098873/cell_15
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
ax=plt.subplots(1,1,figsize=(10,8))
iris['Species'].value_counts().plot.pie(autopct='%1.1f%%',shadow=True,figsize=(10,8))
plt.title("Iris Species %")
plt.show()
iris.shape
plt.figure(figsize=(7, 4))
sns.heatmap(iris.corr(), annot=True, cmap='cubehelix_r')
plt.show()
|
code
|
72098873/cell_16
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
ax=plt.subplots(1,1,figsize=(10,8))
iris['Species'].value_counts().plot.pie(autopct='%1.1f%%',shadow=True,figsize=(10,8))
plt.title("Iris Species %")
plt.show()
iris.shape
train, test = train_test_split(iris, test_size=0.25)
print(train.shape)
print(test.shape)
|
code
|
72098873/cell_3
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
iris['Species'].value_counts()
|
code
|
72098873/cell_14
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
iris.shape
print(iris['Species'].unique())
|
code
|
72098873/cell_22
|
[
"text_plain_output_1.png"
] |
from sklearn import metrics
from sklearn import svm
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
ax=plt.subplots(1,1,figsize=(10,8))
iris['Species'].value_counts().plot.pie(autopct='%1.1f%%',shadow=True,figsize=(10,8))
plt.title("Iris Species %")
plt.show()
iris.shape
train, test = train_test_split(iris, test_size=0.25)
train_X = train[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
train_y = train.Species
test_X = test[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
test_y = test.Species
model = svm.SVC()
model.fit(train_X, train_y)
prediction = model.predict(test_X)
model = LogisticRegression()
model.fit(train_X, train_y)
prediction = model.predict(test_X)
model = DecisionTreeClassifier()
model.fit(train_X, train_y)
prediction = model.predict(test_X)
print('The accuracy of the Decision Tree is', metrics.accuracy_score(prediction, test_y))
|
code
|
72098873/cell_10
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
fig = iris[iris.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa')
iris[iris.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig)
iris[iris.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig)
fig.set_xlabel("Petal Length")
fig.set_ylabel("Petal Width")
fig.set_title(" Petal Length VS Width")
fig=plt.gcf()
fig.set_size_inches(10,6)
plt.show()
iris.drop('Id', axis=1).boxplot(by='Species', figsize=(12, 6))
ax = plt.subplots(1, 1, figsize=(10, 8))
iris['Species'].value_counts().plot.pie(autopct='%1.1f%%', shadow=True, figsize=(10, 8))
plt.title('Iris Species %')
plt.show()
|
code
|
72098873/cell_5
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
iris = pd.read_csv('../input/iris/Iris.csv')
import seaborn as sns
import matplotlib.pyplot as plt
sns.FacetGrid(iris, hue='Species', height=5).map(plt.scatter, 'SepalLengthCm', 'SepalWidthCm').add_legend()
|
code
|
128045865/cell_19
|
[
"text_plain_output_1.png"
] |
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import numpy as np
import pandas as pd
import pickle
import tensorflow as tf
import tensorflow.keras.backend as K
breast_img = glob.glob('/kaggle/input/breast-histopathology-images/IDC_regular_ps50_idx5/**/*.png', recursive=True)
data = pd.read_csv('/kaggle/input/selected-images/selected_images.csv')
train_data, val_data = train_test_split(data, test_size=0.3, random_state=42)
val_data, test_data = train_test_split(val_data, test_size=0.5, random_state=42)
datagen = ImageDataGenerator(rescale=1.0 / 255)
target_size = (50, 50)
batch_size = 32
train_generator = datagen.flow_from_dataframe(dataframe=train_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
val_generator = datagen.flow_from_dataframe(dataframe=val_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
test_generator = datagen.flow_from_dataframe(dataframe=test_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
A, B = np.unique(train_generator.labels, return_counts=True)
n = len(train_generator.labels)
cls_weights = {i: (n - j) / n for i, j in zip(A, B)}
def f1_score(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
recall = true_positives / (possible_positives + K.epsilon())
f1_val = 2 * (precision * recall) / (precision + recall + K.epsilon())
return f1_val
def train_best_model(units, learning_rate):
model = tf.keras.Sequential()
model.add(layers.Conv2D(units, (3, 3), activation='relu', input_shape=(50, 50, 3)))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(units * 2, (3, 3), activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(units * 4, (3, 3), activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(units * 2, activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate), loss='binary_crossentropy', metrics=[tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), f1_score])
history = model.fit(train_generator, validation_data=val_generator, epochs=300, callbacks=tf.keras.callbacks.EarlyStopping(patience=5, min_delta=0.001, monitor='val_loss'), class_weight=cls_weights)
test_results = model.evaluate(test_generator)
return (model, history, test_results)
model.save('cnn_model.h5')
with open('cnn_model_history.pkl', 'wb') as file:
pickle.dump(history.history, file)
with open('cnn_test_results.pkl', 'wb') as file:
pickle.dump(test_results, file)
total_parameters = model.count_params()
mult_adds_total = 0
for layer in model.layers:
if isinstance(layer, tf.keras.layers.Conv2D):
height, width, channels_in = layer.input_shape[1:]
_, _, channels_out = layer.output_shape[1:]
kernel_height, kernel_width = layer.kernel_size
mult_adds = height * width * channels_in * channels_out * kernel_height * kernel_width
mult_adds_total += mult_adds
print('Total parameters:', total_parameters)
print('Total number of multipy-accumulates:', mult_adds_total)
|
code
|
128045865/cell_16
|
[
"text_plain_output_1.png"
] |
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pickle
import tensorflow as tf
import tensorflow.keras.backend as K
breast_img = glob.glob('/kaggle/input/breast-histopathology-images/IDC_regular_ps50_idx5/**/*.png', recursive=True)
data = pd.read_csv('/kaggle/input/selected-images/selected_images.csv')
train_data, val_data = train_test_split(data, test_size=0.3, random_state=42)
val_data, test_data = train_test_split(val_data, test_size=0.5, random_state=42)
datagen = ImageDataGenerator(rescale=1.0 / 255)
target_size = (50, 50)
batch_size = 32
train_generator = datagen.flow_from_dataframe(dataframe=train_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
val_generator = datagen.flow_from_dataframe(dataframe=val_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
test_generator = datagen.flow_from_dataframe(dataframe=test_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
A, B = np.unique(train_generator.labels, return_counts=True)
n = len(train_generator.labels)
cls_weights = {i: (n - j) / n for i, j in zip(A, B)}
def f1_score(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
recall = true_positives / (possible_positives + K.epsilon())
f1_val = 2 * (precision * recall) / (precision + recall + K.epsilon())
return f1_val
def train_best_model(units, learning_rate):
model = tf.keras.Sequential()
model.add(layers.Conv2D(units, (3, 3), activation='relu', input_shape=(50, 50, 3)))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(units * 2, (3, 3), activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(units * 4, (3, 3), activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(units * 2, activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate), loss='binary_crossentropy', metrics=[tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), f1_score])
history = model.fit(train_generator, validation_data=val_generator, epochs=300, callbacks=tf.keras.callbacks.EarlyStopping(patience=5, min_delta=0.001, monitor='val_loss'), class_weight=cls_weights)
test_results = model.evaluate(test_generator)
return (model, history, test_results)
model.save('cnn_model.h5')
with open('cnn_model_history.pkl', 'wb') as file:
pickle.dump(history.history, file)
with open('cnn_test_results.pkl', 'wb') as file:
pickle.dump(test_results, file)
def plot_the_results(history):
plt.style.use('seaborn')
plt.figure(figsize=(10, 5))
plt.plot(history.epoch, history.history['val_f1_score'], label='Val F1 Score', linewidth=2)
plt.plot(history.epoch, history.history['f1_score'], label='f1_score', linewidth=2)
plt.legend()
plt.title('F1 Score')
plt.show()
plt.figure(figsize=(10, 5))
plt.plot(history.epoch, history.history['val_precision'], label='Val Precision', linewidth=2)
plt.plot(history.epoch, history.history['precision'], label='Precision', linewidth=2)
plt.legend()
plt.title('Precision')
plt.show()
plt.figure(figsize=(10, 5))
plt.plot(history.epoch, history.history['val_recall'], label='Val Recall', linewidth=2)
plt.plot(history.epoch, history.history['recall'], label='Recall', linewidth=2)
plt.legend()
plt.title('Recall')
plt.show()
plot_the_results(history)
|
code
|
128045865/cell_17
|
[
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] |
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.keras.backend as K
breast_img = glob.glob('/kaggle/input/breast-histopathology-images/IDC_regular_ps50_idx5/**/*.png', recursive=True)
data = pd.read_csv('/kaggle/input/selected-images/selected_images.csv')
train_data, val_data = train_test_split(data, test_size=0.3, random_state=42)
val_data, test_data = train_test_split(val_data, test_size=0.5, random_state=42)
datagen = ImageDataGenerator(rescale=1.0 / 255)
target_size = (50, 50)
batch_size = 32
train_generator = datagen.flow_from_dataframe(dataframe=train_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
val_generator = datagen.flow_from_dataframe(dataframe=val_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
test_generator = datagen.flow_from_dataframe(dataframe=test_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
A, B = np.unique(train_generator.labels, return_counts=True)
n = len(train_generator.labels)
cls_weights = {i: (n - j) / n for i, j in zip(A, B)}
def f1_score(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
recall = true_positives / (possible_positives + K.epsilon())
f1_val = 2 * (precision * recall) / (precision + recall + K.epsilon())
return f1_val
def train_best_model(units, learning_rate):
model = tf.keras.Sequential()
model.add(layers.Conv2D(units, (3, 3), activation='relu', input_shape=(50, 50, 3)))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(units * 2, (3, 3), activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(units * 4, (3, 3), activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(units * 2, activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate), loss='binary_crossentropy', metrics=[tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), f1_score])
history = model.fit(train_generator, validation_data=val_generator, epochs=300, callbacks=tf.keras.callbacks.EarlyStopping(patience=5, min_delta=0.001, monitor='val_loss'), class_weight=cls_weights)
test_results = model.evaluate(test_generator)
return (model, history, test_results)
print('Test Results')
print('\n-------------\n')
print('Test Loss:', format(test_results[0], '.3f'))
print('Test Precision: ', format(test_results[1], '.3f'))
print('Test Recall: ', format(test_results[2], '.3f'))
print('Test F1: ', format(test_results[3], '.3f'))
|
code
|
128045865/cell_14
|
[
"text_plain_output_1.png"
] |
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.keras.backend as K
breast_img = glob.glob('/kaggle/input/breast-histopathology-images/IDC_regular_ps50_idx5/**/*.png', recursive=True)
data = pd.read_csv('/kaggle/input/selected-images/selected_images.csv')
train_data, val_data = train_test_split(data, test_size=0.3, random_state=42)
val_data, test_data = train_test_split(val_data, test_size=0.5, random_state=42)
datagen = ImageDataGenerator(rescale=1.0 / 255)
target_size = (50, 50)
batch_size = 32
train_generator = datagen.flow_from_dataframe(dataframe=train_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
val_generator = datagen.flow_from_dataframe(dataframe=val_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
test_generator = datagen.flow_from_dataframe(dataframe=test_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
A, B = np.unique(train_generator.labels, return_counts=True)
n = len(train_generator.labels)
cls_weights = {i: (n - j) / n for i, j in zip(A, B)}
def f1_score(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
recall = true_positives / (possible_positives + K.epsilon())
f1_val = 2 * (precision * recall) / (precision + recall + K.epsilon())
return f1_val
def train_best_model(units, learning_rate):
model = tf.keras.Sequential()
model.add(layers.Conv2D(units, (3, 3), activation='relu', input_shape=(50, 50, 3)))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(units * 2, (3, 3), activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(units * 4, (3, 3), activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(units * 2, activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate), loss='binary_crossentropy', metrics=[tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), f1_score])
history = model.fit(train_generator, validation_data=val_generator, epochs=300, callbacks=tf.keras.callbacks.EarlyStopping(patience=5, min_delta=0.001, monitor='val_loss'), class_weight=cls_weights)
test_results = model.evaluate(test_generator)
return (model, history, test_results)
best_settings = {'learning_rate': 0.001, 'units': 64}
model, history, test_results = train_best_model(**best_settings)
|
code
|
128045865/cell_5
|
[
"text_plain_output_1.png"
] |
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import pandas as pd
breast_img = glob.glob('/kaggle/input/breast-histopathology-images/IDC_regular_ps50_idx5/**/*.png', recursive=True)
data = pd.read_csv('/kaggle/input/selected-images/selected_images.csv')
train_data, val_data = train_test_split(data, test_size=0.3, random_state=42)
val_data, test_data = train_test_split(val_data, test_size=0.5, random_state=42)
datagen = ImageDataGenerator(rescale=1.0 / 255)
target_size = (50, 50)
batch_size = 32
train_generator = datagen.flow_from_dataframe(dataframe=train_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
val_generator = datagen.flow_from_dataframe(dataframe=val_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
test_generator = datagen.flow_from_dataframe(dataframe=test_data, x_col='path', y_col='label', target_size=target_size, batch_size=batch_size, class_mode='raw')
|
code
|
2017383/cell_4
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.info()
|
code
|
2017383/cell_6
|
[
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data['Title'] = all_data['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
stat_min = 10
title_names = all_data['Title'].value_counts() < stat_min
all_data['Title'] = all_data['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)
print(all_data['Title'].value_counts())
all_data = all_data.drop(['Name'], axis=1)
all_data = all_data.drop(['Ticket'], axis=1)
all_data = all_data.drop(['Cabin'], axis=1)
all_data.info()
|
code
|
2017383/cell_11
|
[
"text_plain_output_1.png"
] |
from sklearn import feature_selection
from sklearn import model_selection
from sklearn import tree
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data['Title'] = all_data['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
stat_min = 10
title_names = all_data['Title'].value_counts() < stat_min
all_data['Title'] = all_data['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)
all_data = all_data.drop(['Name'], axis=1)
all_data = all_data.drop(['Ticket'], axis=1)
all_data = all_data.drop(['Cabin'], axis=1)
all_data = pd.get_dummies(all_data)
cv_split = model_selection.ShuffleSplit(n_splits=10, test_size=0.3, train_size=0.6, random_state=0)
train_cleared = all_data[:train.shape[0]]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train_cleared, train.Survived, random_state=0, test_size=0.1)
X_val = all_data[train.shape[0]:]
dtree = tree.DecisionTreeClassifier(random_state=0)
base_results = model_selection.cross_validate(dtree, train_cleared, train.Survived, cv=cv_split, return_train_score=True)
dtree.fit(train_cleared, train.Survived)
param_grid = {'criterion': ['gini', 'entropy'], 'max_depth': [2, 4, 6, 8, 10, None], 'random_state': [0]}
tune_model = model_selection.GridSearchCV(tree.DecisionTreeClassifier(), param_grid=param_grid, scoring='roc_auc', cv=cv_split, return_train_score=True)
tune_model.fit(train_cleared, train.Survived)
print('BEFORE DT RFE Training Shape Old: ', train_cleared.shape)
print('BEFORE DT RFE Training Columns Old: ', train_cleared.columns.values)
print('BEFORE DT RFE Training w/bin score mean: {:.2f}'.format(base_results['train_score'].mean() * 100))
print('BEFORE DT RFE Test w/bin score mean: {:.2f}'.format(base_results['test_score'].mean() * 100))
print('BEFORE DT RFE Test w/bin score 3*std: +/- {:.2f}'.format(base_results['test_score'].std() * 100 * 3))
print('-' * 10)
dtree_rfe = feature_selection.RFECV(dtree, step=1, scoring='accuracy', cv=cv_split)
dtree_rfe.fit(train_cleared, train.Survived)
X_rfe = train_cleared.columns.values[dtree_rfe.get_support()]
rfe_results = model_selection.cross_validate(dtree, train_cleared[X_rfe], train.Survived, cv=cv_split)
print('AFTER DT RFE Training Shape New: ', train_cleared[X_rfe].shape)
print('AFTER DT RFE Training Columns New: ', X_rfe)
print('AFTER DT RFE Training w/bin score mean: {:.2f}'.format(rfe_results['train_score'].mean() * 100))
print('AFTER DT RFE Test w/bin score mean: {:.2f}'.format(rfe_results['test_score'].mean() * 100))
print('AFTER DT RFE Test w/bin score 3*std: +/- {:.2f}'.format(rfe_results['test_score'].std() * 100 * 3))
print('-' * 10)
rfe_tune_model = model_selection.GridSearchCV(tree.DecisionTreeClassifier(), param_grid=param_grid, scoring='roc_auc', cv=cv_split)
rfe_tune_model.fit(train_cleared[X_rfe], train.Survived)
print('AFTER DT RFE Tuned Parameters: ', rfe_tune_model.best_params_)
print('AFTER DT RFE Tuned Training w/bin score mean: {:.2f}'.format(rfe_tune_model.cv_results_['mean_train_score'][tune_model.best_index_] * 100))
print('AFTER DT RFE Tuned Test w/bin score mean: {:.2f}'.format(rfe_tune_model.cv_results_['mean_test_score'][tune_model.best_index_] * 100))
print('AFTER DT RFE Tuned Test w/bin score 3*std: +/- {:.2f}'.format(rfe_tune_model.cv_results_['std_test_score'][tune_model.best_index_] * 100 * 3))
print('-' * 10)
|
code
|
2017383/cell_7
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data['Title'] = all_data['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
stat_min = 10
title_names = all_data['Title'].value_counts() < stat_min
all_data['Title'] = all_data['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)
all_data = all_data.drop(['Name'], axis=1)
all_data = all_data.drop(['Ticket'], axis=1)
all_data = all_data.drop(['Cabin'], axis=1)
all_data = pd.get_dummies(all_data)
all_data.head()
|
code
|
2017383/cell_10
|
[
"text_plain_output_1.png"
] |
from sklearn import model_selection
from sklearn import tree
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data['Title'] = all_data['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
stat_min = 10
title_names = all_data['Title'].value_counts() < stat_min
all_data['Title'] = all_data['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)
all_data = all_data.drop(['Name'], axis=1)
all_data = all_data.drop(['Ticket'], axis=1)
all_data = all_data.drop(['Cabin'], axis=1)
all_data = pd.get_dummies(all_data)
cv_split = model_selection.ShuffleSplit(n_splits=10, test_size=0.3, train_size=0.6, random_state=0)
train_cleared = all_data[:train.shape[0]]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train_cleared, train.Survived, random_state=0, test_size=0.1)
X_val = all_data[train.shape[0]:]
dtree = tree.DecisionTreeClassifier(random_state=0)
base_results = model_selection.cross_validate(dtree, train_cleared, train.Survived, cv=cv_split, return_train_score=True)
dtree.fit(train_cleared, train.Survived)
print('BEFORE DT Parameters: ', dtree.get_params())
print('BEFORE DT Training w/bin score mean: {:.2f}'.format(base_results['train_score'].mean() * 100))
print('BEFORE DT Test w/bin score mean: {:.2f}'.format(base_results['test_score'].mean() * 100))
print('BEFORE DT Test w/bin score 3*std: +/- {:.2f}'.format(base_results['test_score'].std() * 100 * 3))
print('-' * 10)
param_grid = {'criterion': ['gini', 'entropy'], 'max_depth': [2, 4, 6, 8, 10, None], 'random_state': [0]}
tune_model = model_selection.GridSearchCV(tree.DecisionTreeClassifier(), param_grid=param_grid, scoring='roc_auc', cv=cv_split, return_train_score=True)
tune_model.fit(train_cleared, train.Survived)
print('AFTER DT Parameters: ', tune_model.best_params_)
print('AFTER DT Training w/bin score mean: {:.2f}'.format(tune_model.cv_results_['mean_train_score'][tune_model.best_index_] * 100))
print('AFTER DT Test w/bin score mean: {:.2f}'.format(tune_model.cv_results_['mean_test_score'][tune_model.best_index_] * 100))
print('AFTER DT Test w/bin score 3*std: +/- {:.2f}'.format(tune_model.cv_results_['std_test_score'][tune_model.best_index_] * 100 * 3))
print('-' * 10)
|
code
|
2017383/cell_12
|
[
"text_plain_output_1.png"
] |
from sklearn import feature_selection
from sklearn import model_selection
from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data['Title'] = all_data['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
stat_min = 10
title_names = all_data['Title'].value_counts() < stat_min
all_data['Title'] = all_data['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)
all_data = all_data.drop(['Name'], axis=1)
all_data = all_data.drop(['Ticket'], axis=1)
all_data = all_data.drop(['Cabin'], axis=1)
all_data = pd.get_dummies(all_data)
cv_split = model_selection.ShuffleSplit(n_splits=10, test_size=0.3, train_size=0.6, random_state=0)
train_cleared = all_data[:train.shape[0]]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(train_cleared, train.Survived, random_state=0, test_size=0.1)
X_val = all_data[train.shape[0]:]
dtree = tree.DecisionTreeClassifier(random_state=0)
base_results = model_selection.cross_validate(dtree, train_cleared, train.Survived, cv=cv_split, return_train_score=True)
dtree.fit(train_cleared, train.Survived)
param_grid = {'criterion': ['gini', 'entropy'], 'max_depth': [2, 4, 6, 8, 10, None], 'random_state': [0]}
tune_model = model_selection.GridSearchCV(tree.DecisionTreeClassifier(), param_grid=param_grid, scoring='roc_auc', cv=cv_split, return_train_score=True)
tune_model.fit(train_cleared, train.Survived)
dtree_rfe = feature_selection.RFECV(dtree, step=1, scoring='accuracy', cv=cv_split)
dtree_rfe.fit(train_cleared, train.Survived)
X_rfe = train_cleared.columns.values[dtree_rfe.get_support()]
rfe_results = model_selection.cross_validate(dtree, train_cleared[X_rfe], train.Survived, cv=cv_split)
rfe_tune_model = model_selection.GridSearchCV(tree.DecisionTreeClassifier(), param_grid=param_grid, scoring='roc_auc', cv=cv_split)
rfe_tune_model.fit(train_cleared[X_rfe], train.Survived)
model = DecisionTreeClassifier(random_state=0, max_depth=5)
model.fit(X_train, y_train)
print('Train score: {:.3f}'.format(model.score(X_train, y_train)))
print('Test score: {:.3f}'.format(model.score(X_test, y_test)))
decision_tree_predicts_base = model.predict(X_val)
decision_tree_predicts_tuned_param = tune_model.predict(X_val)
decision_tree_predicts_tuned_param_rfe = rfe_tune_model.predict(X_val[X_rfe])
|
code
|
2017383/cell_5
|
[
"text_plain_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare = all_data.Fare.fillna(all_data.Fare.median())
all_data.Embarked = all_data.Embarked.fillna(all_data.Embarked.mode()[0])
all_data.info()
|
code
|
33096987/cell_42
|
[
"image_output_1.png"
] |
from collections import Counter
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def barplot(variable):
"""
input : variable example: "Sex"
output : barplot & value count
"""
var = train_df[variable]
varValue = var.value_counts()
plt.xticks(varValue.index, varValue.index.values)
def plothist(variable):
pass
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])]
train_df = train_df.drop(detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
list1 = ['SibSp', 'Age', 'Fare', 'Parch', 'Survived']
sns.heatmap(train_df[list1].corr(), annot=True, fmt='.2f')
|
code
|
33096987/cell_21
|
[
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"image_output_4.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
train_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)
|
code
|
33096987/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)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
train_df.info()
|
code
|
33096987/cell_34
|
[
"text_html_output_1.png"
] |
from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
def detect_outliers(df, features):
outlier_indices = []
for c in features:
q1 = np.percentile(df[c], 25)
q3 = np.percentile(df[c], 75)
IQR = q3 - q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < q1 - outlier_step) | (df[c] > q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
train_df.loc[detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare'])]
train_df = train_df.drop(detect_outliers(train_df, ['Age', 'Parch', 'SibSp', 'Fare']), axis=0).reset_index(drop=True)
train_df_len = len(train_df)
train_df = pd.concat([train_df, test_df], axis=0).reset_index(drop=True)
train_df.columns[train_df.isnull().any()]
train_df.isnull().sum()
train_df[train_df['Embarked'].isnull()]
|
code
|
33096987/cell_23
|
[
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test_df['PassengerId']
train_df.columns
train_df[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)
|
code
|
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