path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
34150890/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import cv2
import keras
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
import keras
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras import regularizers
from sklearn.model_selection import train_test_split
import cv2
import matplotlib.pyplot as plt
import seaborn as sns
from keras.applications import VGG16
from keras import models
from keras import layers
from keras import optimizers
os.listdir('../input/isl-dataset-double-handed')
train_dir = '../input/isl-dataset-double-handed/ISL_Dataset'
def load_unique():
size_img = 224,224
images_for_plot = []
labels_for_plot = []
for folder in os.listdir(train_dir):
for file in os.listdir(train_dir + '/' + folder):
filepath = train_dir + '/' + folder + '/' + file
image = cv2.imread(filepath)
final_img = cv2.resize(image, size_img)
final_img = cv2.cvtColor(final_img, cv2.COLOR_BGR2RGB)
images_for_plot.append(final_img)
labels_for_plot.append(folder)
break
return images_for_plot, labels_for_plot
images_for_plot, labels_for_plot = load_unique()
print("unique_labels = ", labels_for_plot)
fig = plt.figure(figsize = (15,15))
def plot_images(fig, image, label, row, col, index):
fig.add_subplot(row, col, index)
plt.axis('off')
plt.imshow(image)
plt.title(label)
return
image_index = 0
row = 4
col = 6
for i in range(1,25):
plot_images(fig, images_for_plot[image_index], labels_for_plot[image_index], row, col, i)
image_index = image_index + 1
plt.show()
l1 = []
def load_data():
"""
Loads data and preprocess. Returns train and test data along with labels.
"""
images = []
labels = []
size = (224, 224)
for folder in os.listdir(train_dir):
for image in os.listdir(train_dir + '/' + folder):
temp_img = cv2.imread(train_dir + '/' + folder + '/' + image)
temp_img = cv2.resize(temp_img, size)
images.append(temp_img)
labels.append(ord(folder) - 97)
images = np.array(images)
for i in range(len(images)):
images[i] = images[i].astype('float32') / 255
l1 = labels
labels = keras.utils.to_categorical(labels)
X_train, X_test, Y_train, Y_test = train_test_split(images, labels, test_size=0.25)
return (X_train, X_test, Y_train, Y_test, l1)
X_train, X_test, Y_train, Y_test, l1 = load_data() | code |
34150890/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
import keras
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras import regularizers
from sklearn.model_selection import train_test_split
import cv2
import matplotlib.pyplot as plt
import seaborn as sns
from keras.applications import VGG16
from keras import models
from keras import layers
from keras import optimizers
os.listdir('../input/isl-dataset-double-handed') | code |
34150890/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras import layers
from keras import models
from keras.applications import VGG16
from sklearn.model_selection import train_test_split
import cv2
import keras
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
import keras
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras import regularizers
from sklearn.model_selection import train_test_split
import cv2
import matplotlib.pyplot as plt
import seaborn as sns
from keras.applications import VGG16
from keras import models
from keras import layers
from keras import optimizers
os.listdir('../input/isl-dataset-double-handed')
train_dir = '../input/isl-dataset-double-handed/ISL_Dataset'
def load_unique():
size_img = 224,224
images_for_plot = []
labels_for_plot = []
for folder in os.listdir(train_dir):
for file in os.listdir(train_dir + '/' + folder):
filepath = train_dir + '/' + folder + '/' + file
image = cv2.imread(filepath)
final_img = cv2.resize(image, size_img)
final_img = cv2.cvtColor(final_img, cv2.COLOR_BGR2RGB)
images_for_plot.append(final_img)
labels_for_plot.append(folder)
break
return images_for_plot, labels_for_plot
images_for_plot, labels_for_plot = load_unique()
print("unique_labels = ", labels_for_plot)
fig = plt.figure(figsize = (15,15))
def plot_images(fig, image, label, row, col, index):
fig.add_subplot(row, col, index)
plt.axis('off')
plt.imshow(image)
plt.title(label)
return
image_index = 0
row = 4
col = 6
for i in range(1,25):
plot_images(fig, images_for_plot[image_index], labels_for_plot[image_index], row, col, i)
image_index = image_index + 1
plt.show()
l1 = []
def load_data():
"""
Loads data and preprocess. Returns train and test data along with labels.
"""
images = []
labels = []
size = (224, 224)
for folder in os.listdir(train_dir):
for image in os.listdir(train_dir + '/' + folder):
temp_img = cv2.imread(train_dir + '/' + folder + '/' + image)
temp_img = cv2.resize(temp_img, size)
images.append(temp_img)
labels.append(ord(folder) - 97)
images = np.array(images)
for i in range(len(images)):
images[i] = images[i].astype('float32') / 255
l1 = labels
labels = keras.utils.to_categorical(labels)
X_train, X_test, Y_train, Y_test = train_test_split(images, labels, test_size=0.25)
return (X_train, X_test, Y_train, Y_test, l1)
def create_model1():
vgg_conv = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
for layer in vgg_conv.layers[:-4]:
layer.trainable = False
model = models.Sequential()
model.add(vgg_conv)
model.add(layers.Flatten())
model.add(layers.Dense(1024, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(26, activation='softmax'))
model.compile(optimizer='adam', loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])
model.summary()
return model
def fit_model():
model_hist = model.fit(X_train, Y_train, batch_size=64, epochs=8, validation_split=0.15)
return model_hist
model = create_model1()
curr_model_hist = fit_model() | code |
34150890/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras import layers
from keras import models
from keras.applications import VGG16
from sklearn.model_selection import train_test_split
import cv2
import keras
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
import keras
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras import regularizers
from sklearn.model_selection import train_test_split
import cv2
import matplotlib.pyplot as plt
import seaborn as sns
from keras.applications import VGG16
from keras import models
from keras import layers
from keras import optimizers
os.listdir('../input/isl-dataset-double-handed')
train_dir = '../input/isl-dataset-double-handed/ISL_Dataset'
def load_unique():
size_img = 224,224
images_for_plot = []
labels_for_plot = []
for folder in os.listdir(train_dir):
for file in os.listdir(train_dir + '/' + folder):
filepath = train_dir + '/' + folder + '/' + file
image = cv2.imread(filepath)
final_img = cv2.resize(image, size_img)
final_img = cv2.cvtColor(final_img, cv2.COLOR_BGR2RGB)
images_for_plot.append(final_img)
labels_for_plot.append(folder)
break
return images_for_plot, labels_for_plot
images_for_plot, labels_for_plot = load_unique()
print("unique_labels = ", labels_for_plot)
fig = plt.figure(figsize = (15,15))
def plot_images(fig, image, label, row, col, index):
fig.add_subplot(row, col, index)
plt.axis('off')
plt.imshow(image)
plt.title(label)
return
image_index = 0
row = 4
col = 6
for i in range(1,25):
plot_images(fig, images_for_plot[image_index], labels_for_plot[image_index], row, col, i)
image_index = image_index + 1
plt.show()
l1 = []
def load_data():
"""
Loads data and preprocess. Returns train and test data along with labels.
"""
images = []
labels = []
size = (224, 224)
for folder in os.listdir(train_dir):
for image in os.listdir(train_dir + '/' + folder):
temp_img = cv2.imread(train_dir + '/' + folder + '/' + image)
temp_img = cv2.resize(temp_img, size)
images.append(temp_img)
labels.append(ord(folder) - 97)
images = np.array(images)
for i in range(len(images)):
images[i] = images[i].astype('float32') / 255
l1 = labels
labels = keras.utils.to_categorical(labels)
X_train, X_test, Y_train, Y_test = train_test_split(images, labels, test_size=0.25)
return (X_train, X_test, Y_train, Y_test, l1)
def create_model1():
vgg_conv = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
for layer in vgg_conv.layers[:-4]:
layer.trainable = False
model = models.Sequential()
model.add(vgg_conv)
model.add(layers.Flatten())
model.add(layers.Dense(1024, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(26, activation='softmax'))
model.compile(optimizer='adam', loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])
model.summary()
return model
def fit_model():
model_hist = model.fit(X_train, Y_train, batch_size=64, epochs=8, validation_split=0.15)
return model_hist
model = create_model1()
curr_model_hist = fit_model()
evaluate_metrics = model.evaluate(X_test, Y_test)
print('\nEvaluation Accuracy = ', '{:.2f}%'.format(evaluate_metrics[1] * 100), '\nEvaluation loss = ', '{:.6f}'.format(evaluate_metrics[0])) | code |
34150890/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import os
import keras
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras import regularizers
from sklearn.model_selection import train_test_split
import cv2
import matplotlib.pyplot as plt
import seaborn as sns
from keras.applications import VGG16
from keras import models
from keras import layers
from keras import optimizers
os.listdir('../input/isl-dataset-double-handed')
train_dir = '../input/isl-dataset-double-handed/ISL_Dataset'
def load_unique():
size_img = (224, 224)
images_for_plot = []
labels_for_plot = []
for folder in os.listdir(train_dir):
for file in os.listdir(train_dir + '/' + folder):
filepath = train_dir + '/' + folder + '/' + file
image = cv2.imread(filepath)
final_img = cv2.resize(image, size_img)
final_img = cv2.cvtColor(final_img, cv2.COLOR_BGR2RGB)
images_for_plot.append(final_img)
labels_for_plot.append(folder)
break
return (images_for_plot, labels_for_plot)
images_for_plot, labels_for_plot = load_unique()
print('unique_labels = ', labels_for_plot)
fig = plt.figure(figsize=(15, 15))
def plot_images(fig, image, label, row, col, index):
fig.add_subplot(row, col, index)
plt.axis('off')
plt.imshow(image)
plt.title(label)
return
image_index = 0
row = 4
col = 6
for i in range(1, 25):
plot_images(fig, images_for_plot[image_index], labels_for_plot[image_index], row, col, i)
image_index = image_index + 1
plt.show() | code |
89142914/cell_2 | [
"text_html_output_1.png"
] | import os # os python utilities
import warnings
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
from plotly.subplots import make_subplots
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89142914/cell_19 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
fg, ax =plt.subplots(1,2,figsize=(20,7))
ax[0].plot(data['Open'],label='Open',color='green')
ax[0].set_xlabel('Date',size=15)
ax[0].set_ylabel('Price',size=15)
ax[0].legend()
ax[1].plot(data['Close'],label='Close',color='red')
ax[1].set_xlabel('Date',size=15)
ax[1].set_ylabel('Price',size=15)
ax[1].legend()
fg.show()
import plotly.graph_objects as go
fig = go.Figure(data=go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']))
data['SMA5'] = data.Close.rolling(5).mean()
data['SMA20'] = data.Close.rolling(20).mean()
data['SMA50'] = data.Close.rolling(50).mean()
fig = go.Figure(data=[go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='OHLC'), go.Scatter(x=data.Date, y=data.SMA5, line=dict(color='orange', width=1), name='SMA5'), go.Scatter(x=data.Date, y=data.SMA20, line=dict(color='green', width=1), name='SMA20'), go.Scatter(x=data.Date, y=data.SMA50, line=dict(color='blue', width=1), name='SMA50')])
data['EMA5'] = data.Close.ewm(span=5, adjust=False).mean()
data['EMA20'] = data.Close.ewm(span=20, adjust=False).mean()
fig = go.Figure(data=[go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='OHLC'), go.Scatter(x=data.Date, y=data.EMA5, line=dict(color='orange', width=1), name='EMA5'), go.Scatter(x=data.Date, y=data.EMA20, line=dict(color='green', width=1), name='EMA20')])
def bollinger_bands(df, n, m):
TP = (df['High'] + df['Low'] + df['Close']) / 3
data = TP
B_MA = pd.Series(data.rolling(n, min_periods=n).mean(), name='B_MA')
sigma = data.rolling(n, min_periods=n).std()
BU = pd.Series(B_MA + m * sigma, name='BU')
BL = pd.Series(B_MA - m * sigma, name='BL')
df = df.join(B_MA)
df = df.join(BU)
df = df.join(BL)
return df
df = bollinger_bands(data, 20, 2)
plt.figure(figsize=(15, 5))
plt.plot(df['Date'], df['Adj Close'])
plt.title('Price chart (Adj Close) IBEX')
plt.show()
plt.figure(figsize=(15, 5))
plt.title('Bollinger Bands chart IBEX')
plt.plot(df['Date'], df['Adj Close'])
plt.plot(df['Date'], df['BU'], alpha=0.3)
plt.plot(df['Date'], df['BL'], alpha=0.3)
plt.plot(df['Date'], df['B_MA'], alpha=0.3)
plt.fill_between(df['Date'], df['BU'], df['BL'], color='grey', alpha=0.1)
plt.show() | code |
89142914/cell_7 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
data.head() | code |
89142914/cell_18 | [
"image_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
import plotly.graph_objects as go
fig = go.Figure(data=go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']))
data['SMA5'] = data.Close.rolling(5).mean()
data['SMA20'] = data.Close.rolling(20).mean()
data['SMA50'] = data.Close.rolling(50).mean()
fig = go.Figure(data=[go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='OHLC'), go.Scatter(x=data.Date, y=data.SMA5, line=dict(color='orange', width=1), name='SMA5'), go.Scatter(x=data.Date, y=data.SMA20, line=dict(color='green', width=1), name='SMA20'), go.Scatter(x=data.Date, y=data.SMA50, line=dict(color='blue', width=1), name='SMA50')])
data['EMA5'] = data.Close.ewm(span=5, adjust=False).mean()
data['EMA20'] = data.Close.ewm(span=20, adjust=False).mean()
fig = go.Figure(data=[go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='OHLC'), go.Scatter(x=data.Date, y=data.EMA5, line=dict(color='orange', width=1), name='EMA5'), go.Scatter(x=data.Date, y=data.EMA20, line=dict(color='green', width=1), name='EMA20')])
def bollinger_bands(df, n, m):
TP = (df['High'] + df['Low'] + df['Close']) / 3
data = TP
B_MA = pd.Series(data.rolling(n, min_periods=n).mean(), name='B_MA')
sigma = data.rolling(n, min_periods=n).std()
BU = pd.Series(B_MA + m * sigma, name='BU')
BL = pd.Series(B_MA - m * sigma, name='BL')
df = df.join(B_MA)
df = df.join(BU)
df = df.join(BL)
return df
df = bollinger_bands(data, 20, 2)
print(df.tail()) | code |
89142914/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
fg, ax = plt.subplots(1, 2, figsize=(20, 7))
ax[0].plot(data['Open'], label='Open', color='green')
ax[0].set_xlabel('Date', size=15)
ax[0].set_ylabel('Price', size=15)
ax[0].legend()
ax[1].plot(data['Close'], label='Close', color='red')
ax[1].set_xlabel('Date', size=15)
ax[1].set_ylabel('Price', size=15)
ax[1].legend()
fg.show() | code |
89142914/cell_16 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
import plotly.graph_objects as go
fig = go.Figure(data=go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']))
data['SMA5'] = data.Close.rolling(5).mean()
data['SMA20'] = data.Close.rolling(20).mean()
data['SMA50'] = data.Close.rolling(50).mean()
fig = go.Figure(data=[go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='OHLC'), go.Scatter(x=data.Date, y=data.SMA5, line=dict(color='orange', width=1), name='SMA5'), go.Scatter(x=data.Date, y=data.SMA20, line=dict(color='green', width=1), name='SMA20'), go.Scatter(x=data.Date, y=data.SMA50, line=dict(color='blue', width=1), name='SMA50')])
data['EMA5'] = data.Close.ewm(span=5, adjust=False).mean()
data['EMA20'] = data.Close.ewm(span=20, adjust=False).mean()
fig = go.Figure(data=[go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='OHLC'), go.Scatter(x=data.Date, y=data.EMA5, line=dict(color='orange', width=1), name='EMA5'), go.Scatter(x=data.Date, y=data.EMA20, line=dict(color='green', width=1), name='EMA20')])
fig = make_subplots(specs=[[{'secondary_y': True}]])
fig.add_trace(go.Candlestick(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']), secondary_y=True)
fig.add_trace(go.Bar(x=data['Date'], y=data['Volume']), secondary_y=False)
fig.layout.yaxis2.showgrid = False
fig.show() | code |
89142914/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
data.head() | code |
89142914/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
import plotly.graph_objects as go
fig = go.Figure(data=go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']))
data['SMA5'] = data.Close.rolling(5).mean()
data['SMA20'] = data.Close.rolling(20).mean()
data['SMA50'] = data.Close.rolling(50).mean()
fig = go.Figure(data=[go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='OHLC'), go.Scatter(x=data.Date, y=data.SMA5, line=dict(color='orange', width=1), name='SMA5'), go.Scatter(x=data.Date, y=data.SMA20, line=dict(color='green', width=1), name='SMA20'), go.Scatter(x=data.Date, y=data.SMA50, line=dict(color='blue', width=1), name='SMA50')])
data['EMA5'] = data.Close.ewm(span=5, adjust=False).mean()
data['EMA20'] = data.Close.ewm(span=20, adjust=False).mean()
fig = go.Figure(data=[go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='OHLC'), go.Scatter(x=data.Date, y=data.EMA5, line=dict(color='orange', width=1), name='EMA5'), go.Scatter(x=data.Date, y=data.EMA20, line=dict(color='green', width=1), name='EMA20')])
fig.show() | code |
89142914/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
import plotly.graph_objects as go
fig = go.Figure(data=go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']))
fig.show() | code |
89142914/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
import plotly.graph_objects as go
fig = go.Figure(data=go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close']))
data['SMA5'] = data.Close.rolling(5).mean()
data['SMA20'] = data.Close.rolling(20).mean()
data['SMA50'] = data.Close.rolling(50).mean()
fig = go.Figure(data=[go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='OHLC'), go.Scatter(x=data.Date, y=data.SMA5, line=dict(color='orange', width=1), name='SMA5'), go.Scatter(x=data.Date, y=data.SMA20, line=dict(color='green', width=1), name='SMA20'), go.Scatter(x=data.Date, y=data.SMA50, line=dict(color='blue', width=1), name='SMA50')])
fig.show() | code |
89142914/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/ibex3519942020/IBEX-2021.csv')
data.info() | code |
32063570/cell_21 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
lgr = LogisticRegression()
lgr.fit(x_test, y_test)
y_pred = lgr.predict(x_test)
cm = confusion_matrix(y_test, y_pred)
conf_matrix = pd.DataFrame(data=cm, columns=['Predicted:0', 'Predicted:1'], index=['Actual:0', 'Actual:1'])
plt.figure(figsize=(8, 5))
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='YlGnBu') | code |
32063570/cell_13 | [
"image_output_1.png"
] | from statsmodels.tools import add_constant
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as st
import statsmodels.api as sm
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
from statsmodels.tools import add_constant
df_constant = add_constant(df)
st.chisqprob = lambda chisq, df: st.chi2.sf(chisq, df)
cols = df_constant.columns[:-1]
model = sm.Logit(df.TenYearCHD, df_constant[cols])
r = model.fit()
r.summary() | code |
32063570/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
sns.countplot(x='TenYearCHD', data=df) | code |
32063570/cell_25 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
lgr = LogisticRegression()
lgr.fit(x_test, y_test)
y_pred = lgr.predict(x_test)
cm = confusion_matrix(y_test, y_pred)
conf_matrix = pd.DataFrame(data=cm, columns=['Predicted:0', 'Predicted:1'], index=['Actual:0', 'Actual:1'])
y_pred_prob = lgr.predict_proba(x_test)[:, :]
y_pred_prob_df = pd.DataFrame(data=y_pred_prob, columns=['No Heart Disease (0)', 'Heart Disease (1)'])
y_pred_prob_df.head() | code |
32063570/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum() | code |
32063570/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
lgr = LogisticRegression()
lgr.fit(x_test, y_test)
y_pred = lgr.predict(x_test)
cm = confusion_matrix(y_test, y_pred)
conf_matrix = pd.DataFrame(data=cm, columns=['Predicted:0', 'Predicted:1'], index=['Actual:0', 'Actual:1'])
TN = cm[0, 0]
TP = cm[1, 1]
FN = cm[1, 0]
FP = cm[0, 1]
sensitivity = TP / float(TP + FN)
specificity = TN / float(TN + FP)
print('The accuracy of the model = TP+TN/(TP+TN+FP+FN) = ', (TP + TN) / float(TP + TN + FP + FN), '\n', 'The Missclassification = 1-Accuracy = ', 1 - (TP + TN) / float(TP + TN + FP + FN), '\n', 'Sensitivity or True Positive Rate = TP/(TP+FN) = ', TP / float(TP + FN), '\n', 'Specificity or True Negative Rate = TN/(TN+FP) = ', TN / float(TN + FP), '\n', 'Positive Predictive value = TP/(TP+FP) = ', TP / float(TP + FP), '\n', 'Negative predictive Value = TN/(TN+FN) = ', TN / float(TN + FN), '\n', 'Positive Likelihood Ratio = Sensitivity/(1-Specificity) = ', sensitivity / (1 - specificity), '\n', 'Negative likelihood Ratio = (1-Sensitivity)/Specificity = ', (1 - sensitivity) / specificity) | code |
32063570/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import binarize
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import sklearn
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
lgr = LogisticRegression()
lgr.fit(x_test, y_test)
y_pred = lgr.predict(x_test)
sklearn.metrics.accuracy_score(y_test, y_pred)
cm = confusion_matrix(y_test, y_pred)
conf_matrix = pd.DataFrame(data=cm, columns=['Predicted:0', 'Predicted:1'], index=['Actual:0', 'Actual:1'])
y_pred_prob = lgr.predict_proba(x_test)[:, :]
y_pred_prob_df = pd.DataFrame(data=y_pred_prob, columns=['No Heart Disease (0)', 'Heart Disease (1)'])
for i in range(1, 5):
cm2 = 0
y_pred_prob_yes = lgr.predict_proba(x_test)
y_pred2 = binarize(y_pred_prob_yes, i / 10)[:, 1]
cm2 = confusion_matrix(y_test, y_pred2)
sklearn.metrics.roc_auc_score(y_test, y_pred_prob_yes[:, 1]) | code |
32063570/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.head() | code |
32063570/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
df.describe() | code |
32063570/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import sklearn
lgr = LogisticRegression()
lgr.fit(x_test, y_test)
y_pred = lgr.predict(x_test)
sklearn.metrics.accuracy_score(y_test, y_pred) | code |
32063570/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import scipy.stats as st
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix
import matplotlib.mlab as mlab | code |
32063570/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe, features, rows, cols):
fig = plt.figure(figsize=(20, 20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows, cols, i + 1)
dataframe[feature].hist(bins=20, ax=a, facecolor='green')
a.set_title(feature + 'Distribution', color='blue')
fig.tight_layout()
plt.show()
draw_histograms(df, df.columns, 6, 3) | code |
32063570/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve
from sklearn.preprocessing import binarize
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
lgr = LogisticRegression()
lgr.fit(x_test, y_test)
y_pred = lgr.predict(x_test)
cm = confusion_matrix(y_test, y_pred)
conf_matrix = pd.DataFrame(data=cm, columns=['Predicted:0', 'Predicted:1'], index=['Actual:0', 'Actual:1'])
y_pred_prob = lgr.predict_proba(x_test)[:, :]
y_pred_prob_df = pd.DataFrame(data=y_pred_prob, columns=['No Heart Disease (0)', 'Heart Disease (1)'])
for i in range(1, 5):
cm2 = 0
y_pred_prob_yes = lgr.predict_proba(x_test)
y_pred2 = binarize(y_pred_prob_yes, i / 10)[:, 1]
cm2 = confusion_matrix(y_test, y_pred2)
from sklearn.metrics import roc_curve
fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob_yes[:, 1])
plt.plot(fpr, tpr)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.title('ROC curve for Heart disease classifier')
plt.xlabel('False positive rate (1-Specificity)')
plt.ylabel('True positive rate (Sensitivity)')
plt.grid(True) | code |
32063570/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts() | code |
32063570/cell_14 | [
"text_plain_output_1.png"
] | from statsmodels.tools import add_constant
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as st
import statsmodels.api as sm
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
from statsmodels.tools import add_constant
df_constant = add_constant(df)
st.chisqprob = lambda chisq, df: st.chi2.sf(chisq, df)
cols = df_constant.columns[:-1]
model = sm.Logit(df.TenYearCHD, df_constant[cols])
r = model.fit()
r.summary()
p = np.exp(r.params)
conf = np.exp(r.conf_int())
conf['OR'] = p
pv = round(r.pvalues, 3)
conf['pvalue'] = pv
conf.columns = ['CI 95%(2.5%)', 'CI 95%(97.5%)', 'Odds Ratio', 'pvalue']
print(conf) | code |
32063570/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
sns.pairplot(df) | code |
32063570/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import binarize
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
lgr = LogisticRegression()
lgr.fit(x_test, y_test)
y_pred = lgr.predict(x_test)
cm = confusion_matrix(y_test, y_pred)
conf_matrix = pd.DataFrame(data=cm, columns=['Predicted:0', 'Predicted:1'], index=['Actual:0', 'Actual:1'])
y_pred_prob = lgr.predict_proba(x_test)[:, :]
y_pred_prob_df = pd.DataFrame(data=y_pred_prob, columns=['No Heart Disease (0)', 'Heart Disease (1)'])
for i in range(1, 5):
cm2 = 0
y_pred_prob_yes = lgr.predict_proba(x_test)
y_pred2 = binarize(y_pred_prob_yes, i / 10)[:, 1]
cm2 = confusion_matrix(y_test, y_pred2)
print('With', i / 10, 'threshold the Confusion Matrix is ', '\n', cm2, '\n', 'with', cm2[0, 0] + cm2[1, 1], 'correct predictions and', cm2[1, 0], 'Type II errors( False Negatives)', '\n\n', 'Sensitivity: ', cm2[1, 1] / float(cm2[1, 1] + cm2[1, 0]), 'Specificity: ', cm2[0, 0] / float(cm2[0, 0] + cm2[0, 1]), '\n\n\n') | code |
32063570/cell_12 | [
"text_plain_output_1.png"
] | from statsmodels.tools import add_constant
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
df.dropna(axis=0, inplace=True)
def draw_histograms(dataframe,features,rows,cols):
fig = plt.figure(figsize = (20,20))
for i, feature in enumerate(features):
a = fig.add_subplot(rows,cols,i+1)
dataframe[feature].hist(bins = 20,ax=a,facecolor = 'green')
a.set_title(feature + "Distribution",color = 'blue')
fig.tight_layout()
plt.show()
draw_histograms(df,df.columns,6,3)
df.TenYearCHD.value_counts()
from statsmodels.tools import add_constant
df_constant = add_constant(df)
df_constant.head() | code |
32063570/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/framingham.csv')
df.drop(['education'], axis=1, inplace=True)
df.rename(columns={'male': 'Sex_male'}, inplace=True)
df.isnull().sum()
count = 0
for i in df.isnull().sum(axis=1):
count = count + 1
print('Total number of missing values:', count) | code |
90118273/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/sample_submission.csv')
missing_values_train = train.isna().any().sum()
missing_values_test = test.isna().any().sum()
duplicates_train = train.duplicated().sum()
print('Duplicates in train data: {0}'.format(duplicates_train))
duplicates_test = test.duplicated().sum()
print('Duplicates in test data: {0}'.format(duplicates_test)) | code |
90118273/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/sample_submission.csv')
print('Train data shape:', train.shape)
print('Test data shape:', test.shape) | code |
90118273/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/sample_submission.csv')
train.describe() | code |
90118273/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/sample_submission.csv')
missing_values_train = train.isna().any().sum()
print('Missing values in train data: {0}'.format(missing_values_train[missing_values_train > 0]))
missing_values_test = test.isna().any().sum()
print('Missing values in test data: {0}'.format(missing_values_test[missing_values_test > 0])) | code |
90118273/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/sample_submission.csv')
print('Columns: \n{0}'.format(list(train.columns))) | code |
90118273/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', index_col='row_id', parse_dates=['time'])
test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv', index_col='row_id', parse_dates=['time'])
sub = pd.read_csv('../input/tabular-playground-series-mar-2022/sample_submission.csv')
train.head() | code |
128005453/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
g = sns.pairplot(hrp, kind='reg', diag_kws={'color': 'red'})
g.fig.suptitle('Correlation of House rent prediction Dataset', y=1.08)
plt.show() | code |
128005453/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Furnishing Status'].value_counts() | code |
128005453/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Area Locality'].value_counts() | code |
128005453/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.describe() | code |
128005453/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.params
model.summary().tables[1]
model.f_pvalue
model.fvalue
model.conf_int()
model.f_pvalue
model.tvalues | code |
128005453/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
g= sns.pairplot(hrp,kind="reg",diag_kws= {'color': 'red'})
g.fig.suptitle("Correlation of House rent prediction Dataset", y=1.08)
plt.show()
import seaborn as sns
sns.pairplot(hrp, hue='Rent', corner=True) | code |
128005453/cell_30 | [
"image_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.params
model.summary().tables[1]
model.f_pvalue
print('f_pvalue: ', '%.4f' % model.f_pvalue) | code |
128005453/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.params
model.summary().tables[1]
model.f_pvalue
model.fvalue
model.conf_int()
model.f_pvalue | code |
128005453/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr() | code |
128005453/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes | code |
128005453/cell_29 | [
"image_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.params
model.summary().tables[1]
model.f_pvalue | code |
128005453/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary() | code |
128005453/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.head() | code |
128005453/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['City'].value_counts() | code |
128005453/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp['City'].value_counts().plot.pie() | code |
128005453/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape | code |
128005453/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp['Area Locality'].value_counts() | code |
128005453/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.params
model.summary().tables[1]
model.f_pvalue
model.fvalue
model.conf_int() | code |
128005453/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.params
model.summary().tables[1] | code |
128005453/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Floor'].value_counts() | code |
128005453/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna() | code |
128005453/cell_3 | [
"image_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.tail() | code |
128005453/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum() | code |
128005453/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.params
model.summary().tables[1]
model.f_pvalue
model.fvalue
model.conf_int()
model.f_pvalue
model.tvalues
model.mse_model | code |
128005453/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.params
model.summary().tables[1]
model.f_pvalue
model.fvalue | code |
128005453/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
X.head() | code |
128005453/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Point of Contact'].value_counts() | code |
128005453/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
g= sns.pairplot(hrp,kind="reg",diag_kws= {'color': 'red'})
g.fig.suptitle("Correlation of House rent prediction Dataset", y=1.08)
plt.show()
sns.jointplot(x='BHK', y='Rent', data=hrp, kind='reg', color='green')
plt.show() | code |
128005453/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Area Type'].value_counts() | code |
128005453/cell_27 | [
"image_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp.isna()
hrp.isna().sum()
hrp.corr()
X = hrp.BHK
X = sm.add_constant(X)
y = hrp.Rent
slr = sm.OLS(y, X)
model = slr.fit()
model.summary()
model.params | code |
128005453/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.dtypes
hrp.shape
hrp['Tenant Preferred'].value_counts() | code |
128005453/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
hrp = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
hrp.info() | code |
105201509/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/movies/Movies.csv')
movies
#Converting a column of the dataframe to a list
genres_list = movies["genres"].head(10).to_list()
language_list = movies["language"].unique()
movies["budget"] = movies["budget"].astype(int)
budget_value = movies["budget"].head(5).to_list()
language_list | code |
105201509/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/movies/Movies.csv')
movies | code |
105201509/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 |
105201509/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/movies/Movies.csv')
movies
movies.columns.to_list().index('imdb_score') | code |
105201509/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/movies/Movies.csv')
movies
Gross_amount = movies['gross'].sort_values(ascending=False)
Gross_amount.head(10) | code |
105201509/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
movies = pd.read_csv('/kaggle/input/movies/Movies.csv')
movies
#Converting a column of the dataframe to a list
genres_list = movies["genres"].head(10).to_list()
language_list = movies["language"].unique()
movies["budget"] = movies["budget"].astype(int)
budget_value = movies["budget"].head(5).to_list()
genres_list | code |
88086475/cell_21 | [
"text_plain_output_1.png"
] | # Generates metadata for test images.
path_to_test_metadata = "/kaggle/working/test.csv"
!echo "image,species,individual_id" > {path_to_test_metadata}
!ls {path_to_inputs}/test_images | sed "s/.jpg/.jpg,unknown,unknown/g" >> {path_to_test_metadata}
# Shows contents of generated metadata.
!head {path_to_test_metadata} | code |
88086475/cell_13 | [
"text_plain_output_1.png"
] | !head {path_to_inputs}/sample_submission.csv | code |
88086475/cell_25 | [
"text_plain_output_1.png"
] | # Installs required libraries.
!pip install numpy
!pip install pandas
!pip install keras
!pip install Pillow
!pip install imagehash
!pip install sewar
!pip install plotly | code |
88086475/cell_33 | [
"text_plain_output_1.png"
] | path_to_metadata = '%s/train.csv' % path_to_inputs
path_to_dir_images = '%s/train_images' % path_to_inputs
whale_and_dolphin = WhaleAndDolphin(path_to_metadata=path_to_metadata, path_to_dir_images=path_to_dir_images)
all_species = whale_and_dolphin.getAllSpecies()
print('Number of species:')
print(len(all_species))
print()
print('Name of species:')
print(all_species)
print() | code |
88086475/cell_44 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"text_plain_output_5.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"image_output_17.png",
"image_output_30.png",
"text_plain_output_9.png",
"image_output_14.png",
"image_output_28.png",
"text_plain_output_20.png",
"image_output_23.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.png",
"image_output_5.png",
"text_plain_output_14.png",
"image_output_18.png",
"text_plain_output_29.png",
"image_output_21.png",
"text_plain_output_27.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_24.png",
"text_plain_output_21.png",
"text_plain_output_25.png",
"image_output_20.png",
"text_plain_output_18.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_22.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_16.png",
"image_output_16.png",
"text_plain_output_8.png",
"text_plain_output_26.png",
"image_output_27.png",
"image_output_6.png",
"text_plain_output_23.png",
"image_output_12.png",
"text_plain_output_28.png",
"image_output_22.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_29.png",
"text_plain_output_19.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"text_plain_output_17.png",
"text_plain_output_11.png",
"text_plain_output_12.png",
"image_output_15.png",
"image_output_9.png",
"image_output_19.png",
"image_output_26.png"
] | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir_images = path_to_dir_images
self._metadata = pd.read_csv(path_to_metadata)
def getAllSpecies(self):
return self._metadata["species"].unique()
def sliceMetadata(self, query):
return self._metadata.query(query).reset_index(drop=True)
def getAllIndividualIDs(self, metadata):
return metadata["individual_id"].unique()
def showImagesTile(self, metadata, num_cols=3):
num_rows = len(metadata) // num_cols + 1
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
for row in metadata.itertuples():
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, row.Index + 1)
ax.set_title(title)
plt.imshow(image)
plt.show()
plt.clf()
plt.close()
def getImage(self, metadata_row):
title = "%s (%s)" % (metadata_row.individual_id, \
metadata_row.species)
path_to_image = self._pathToImage(
self._path_to_dir_images,
file_name=metadata_row.image
)
image = Image.open(path_to_image)
return title, image
def _pathToImage(self, path_to_dir_images, file_name):
return "%s/%s" % (path_to_dir_images, file_name)
def showIndividualImagesTile(self, metadata, num_cols=3, num_individuals=10):
metadata_sorted = \
metadata.sort_values(by=["individual_id"]).reset_index(drop=True)
num_rows = num_cols * num_individuals
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
num_images = 0
individual_id_prev = ""
i_row, i_col = 0, 0
for row in metadata_sorted.itertuples():
if row.individual_id != individual_id_prev:
# Moves to next row, if different individuals is found.
i_row += 1
i_col = 1
i = (i_row - 1) * num_cols + i_col
individual_id_prev = row.individual_id
if i_row > num_individuals:
break
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print("New individuals is found!!")
#print("> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
elif i_col < num_cols:
# Moves to next column, if the number of images for same individuals is less than num_cols.
i_col += 1
i = (i_row - 1) * num_cols + i_col
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print(">> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
else:
#print("< i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
continue
plt.show()
plt.clf()
plt.close()
path_to_metadata = '%s/train.csv' % path_to_inputs
path_to_dir_images = '%s/train_images' % path_to_inputs
whale_and_dolphin = WhaleAndDolphin(path_to_metadata=path_to_metadata, path_to_dir_images=path_to_dir_images)
all_species = whale_and_dolphin.getAllSpecies()
metadata = {}
stats = pd.DataFrame(columns=['num_of_images', 'num_of_individuals'], index=all_species)
for species in all_species:
metadata[species] = whale_and_dolphin.sliceMetadata(query='species == @species')
num_images = len(metadata[species])
individual_ids = whale_and_dolphin.getAllIndividualIDs(metadata[species])
num_individuals = len(individual_ids)
stats.loc[species] = [num_images, num_individuals]
stats.loc['total'] = [stats['num_of_images'].sum(), stats['num_of_individuals'].sum()]
species = 'melon_headed_whale'
individual_ids = whale_and_dolphin.getAllIndividualIDs(metadata[species])
stats = pd.DataFrame(columns=['num_of_images'], index=individual_ids)
for individual_id in individual_ids:
metadata_individual = metadata[species].query('individual_id == @individual_id').reset_index(drop=True)
num_images = len(metadata_individual)
stats.loc[individual_id] = [num_images]
stats.loc['total'] = [stats['num_of_images'].sum()]
for species in all_species:
print('Images for %s :' % species)
whale_and_dolphin.showIndividualImagesTile(metadata=metadata[species], num_cols=4, num_individuals=10)
print() | code |
88086475/cell_41 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir_images = path_to_dir_images
self._metadata = pd.read_csv(path_to_metadata)
def getAllSpecies(self):
return self._metadata["species"].unique()
def sliceMetadata(self, query):
return self._metadata.query(query).reset_index(drop=True)
def getAllIndividualIDs(self, metadata):
return metadata["individual_id"].unique()
def showImagesTile(self, metadata, num_cols=3):
num_rows = len(metadata) // num_cols + 1
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
for row in metadata.itertuples():
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, row.Index + 1)
ax.set_title(title)
plt.imshow(image)
plt.show()
plt.clf()
plt.close()
def getImage(self, metadata_row):
title = "%s (%s)" % (metadata_row.individual_id, \
metadata_row.species)
path_to_image = self._pathToImage(
self._path_to_dir_images,
file_name=metadata_row.image
)
image = Image.open(path_to_image)
return title, image
def _pathToImage(self, path_to_dir_images, file_name):
return "%s/%s" % (path_to_dir_images, file_name)
def showIndividualImagesTile(self, metadata, num_cols=3, num_individuals=10):
metadata_sorted = \
metadata.sort_values(by=["individual_id"]).reset_index(drop=True)
num_rows = num_cols * num_individuals
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
num_images = 0
individual_id_prev = ""
i_row, i_col = 0, 0
for row in metadata_sorted.itertuples():
if row.individual_id != individual_id_prev:
# Moves to next row, if different individuals is found.
i_row += 1
i_col = 1
i = (i_row - 1) * num_cols + i_col
individual_id_prev = row.individual_id
if i_row > num_individuals:
break
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print("New individuals is found!!")
#print("> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
elif i_col < num_cols:
# Moves to next column, if the number of images for same individuals is less than num_cols.
i_col += 1
i = (i_row - 1) * num_cols + i_col
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print(">> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
else:
#print("< i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
continue
plt.show()
plt.clf()
plt.close()
path_to_metadata = '%s/train.csv' % path_to_inputs
path_to_dir_images = '%s/train_images' % path_to_inputs
whale_and_dolphin = WhaleAndDolphin(path_to_metadata=path_to_metadata, path_to_dir_images=path_to_dir_images)
all_species = whale_and_dolphin.getAllSpecies()
metadata = {}
stats = pd.DataFrame(columns=['num_of_images', 'num_of_individuals'], index=all_species)
for species in all_species:
metadata[species] = whale_and_dolphin.sliceMetadata(query='species == @species')
num_images = len(metadata[species])
individual_ids = whale_and_dolphin.getAllIndividualIDs(metadata[species])
num_individuals = len(individual_ids)
stats.loc[species] = [num_images, num_individuals]
stats.loc['total'] = [stats['num_of_images'].sum(), stats['num_of_individuals'].sum()]
species = 'melon_headed_whale'
individual_ids = whale_and_dolphin.getAllIndividualIDs(metadata[species])
stats = pd.DataFrame(columns=['num_of_images'], index=individual_ids)
for individual_id in individual_ids:
metadata_individual = metadata[species].query('individual_id == @individual_id').reset_index(drop=True)
num_images = len(metadata_individual)
stats.loc[individual_id] = [num_images]
stats.loc['total'] = [stats['num_of_images'].sum()]
pd.set_option('display.max_rows', None)
print('Number of images for each individual of %s:' % species)
print('(Number of individuals for %s: %d)' % (species, len(stats) - 1))
stats | code |
88086475/cell_11 | [
"text_plain_output_1.png"
] | path_to_inputs = "/kaggle/input/happy-whale-and-dolphin"
!ls {path_to_inputs} | code |
88086475/cell_19 | [
"text_plain_output_1.png"
] | !echo "Number of train_images:"
!ls {path_to_inputs}/train_images | cat -n | tail -1 | cut -f1
!echo ""
!echo "Number of test_images:"
!ls {path_to_inputs}/test_images | cat -n | tail -1 | cut -f1 | code |
88086475/cell_50 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir_images = path_to_dir_images
self._metadata = pd.read_csv(path_to_metadata)
def getAllSpecies(self):
return self._metadata["species"].unique()
def sliceMetadata(self, query):
return self._metadata.query(query).reset_index(drop=True)
def getAllIndividualIDs(self, metadata):
return metadata["individual_id"].unique()
def showImagesTile(self, metadata, num_cols=3):
num_rows = len(metadata) // num_cols + 1
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
for row in metadata.itertuples():
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, row.Index + 1)
ax.set_title(title)
plt.imshow(image)
plt.show()
plt.clf()
plt.close()
def getImage(self, metadata_row):
title = "%s (%s)" % (metadata_row.individual_id, \
metadata_row.species)
path_to_image = self._pathToImage(
self._path_to_dir_images,
file_name=metadata_row.image
)
image = Image.open(path_to_image)
return title, image
def _pathToImage(self, path_to_dir_images, file_name):
return "%s/%s" % (path_to_dir_images, file_name)
def showIndividualImagesTile(self, metadata, num_cols=3, num_individuals=10):
metadata_sorted = \
metadata.sort_values(by=["individual_id"]).reset_index(drop=True)
num_rows = num_cols * num_individuals
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
num_images = 0
individual_id_prev = ""
i_row, i_col = 0, 0
for row in metadata_sorted.itertuples():
if row.individual_id != individual_id_prev:
# Moves to next row, if different individuals is found.
i_row += 1
i_col = 1
i = (i_row - 1) * num_cols + i_col
individual_id_prev = row.individual_id
if i_row > num_individuals:
break
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print("New individuals is found!!")
#print("> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
elif i_col < num_cols:
# Moves to next column, if the number of images for same individuals is less than num_cols.
i_col += 1
i = (i_row - 1) * num_cols + i_col
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print(">> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
else:
#print("< i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
continue
plt.show()
plt.clf()
plt.close()
path_to_metadata = '%s/train.csv' % path_to_inputs
path_to_dir_images = '%s/train_images' % path_to_inputs
whale_and_dolphin = WhaleAndDolphin(path_to_metadata=path_to_metadata, path_to_dir_images=path_to_dir_images)
all_species = whale_and_dolphin.getAllSpecies()
metadata = {}
stats = pd.DataFrame(columns=['num_of_images', 'num_of_individuals'], index=all_species)
for species in all_species:
metadata[species] = whale_and_dolphin.sliceMetadata(query='species == @species')
num_images = len(metadata[species])
individual_ids = whale_and_dolphin.getAllIndividualIDs(metadata[species])
num_individuals = len(individual_ids)
stats.loc[species] = [num_images, num_individuals]
stats.loc['total'] = [stats['num_of_images'].sum(), stats['num_of_individuals'].sum()]
species = 'melon_headed_whale'
individual_ids = whale_and_dolphin.getAllIndividualIDs(metadata[species])
stats = pd.DataFrame(columns=['num_of_images'], index=individual_ids)
for individual_id in individual_ids:
metadata_individual = metadata[species].query('individual_id == @individual_id').reset_index(drop=True)
num_images = len(metadata_individual)
stats.loc[individual_id] = [num_images]
stats.loc['total'] = [stats['num_of_images'].sum()]
path_to_metadata = '%s' % path_to_test_metadata
path_to_dir_images = '%s/test_images' % path_to_inputs
whale_and_dolphin = WhaleAndDolphin(path_to_metadata=path_to_metadata, path_to_dir_images=path_to_dir_images)
all_species = whale_and_dolphin.getAllSpecies()
print('Number of species:')
print(len(all_species))
print()
print('All species:')
print(all_species)
print() | code |
88086475/cell_51 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir_images = path_to_dir_images
self._metadata = pd.read_csv(path_to_metadata)
def getAllSpecies(self):
return self._metadata["species"].unique()
def sliceMetadata(self, query):
return self._metadata.query(query).reset_index(drop=True)
def getAllIndividualIDs(self, metadata):
return metadata["individual_id"].unique()
def showImagesTile(self, metadata, num_cols=3):
num_rows = len(metadata) // num_cols + 1
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
for row in metadata.itertuples():
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, row.Index + 1)
ax.set_title(title)
plt.imshow(image)
plt.show()
plt.clf()
plt.close()
def getImage(self, metadata_row):
title = "%s (%s)" % (metadata_row.individual_id, \
metadata_row.species)
path_to_image = self._pathToImage(
self._path_to_dir_images,
file_name=metadata_row.image
)
image = Image.open(path_to_image)
return title, image
def _pathToImage(self, path_to_dir_images, file_name):
return "%s/%s" % (path_to_dir_images, file_name)
def showIndividualImagesTile(self, metadata, num_cols=3, num_individuals=10):
metadata_sorted = \
metadata.sort_values(by=["individual_id"]).reset_index(drop=True)
num_rows = num_cols * num_individuals
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
num_images = 0
individual_id_prev = ""
i_row, i_col = 0, 0
for row in metadata_sorted.itertuples():
if row.individual_id != individual_id_prev:
# Moves to next row, if different individuals is found.
i_row += 1
i_col = 1
i = (i_row - 1) * num_cols + i_col
individual_id_prev = row.individual_id
if i_row > num_individuals:
break
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print("New individuals is found!!")
#print("> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
elif i_col < num_cols:
# Moves to next column, if the number of images for same individuals is less than num_cols.
i_col += 1
i = (i_row - 1) * num_cols + i_col
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print(">> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
else:
#print("< i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
continue
plt.show()
plt.clf()
plt.close()
path_to_metadata = '%s/train.csv' % path_to_inputs
path_to_dir_images = '%s/train_images' % path_to_inputs
whale_and_dolphin = WhaleAndDolphin(path_to_metadata=path_to_metadata, path_to_dir_images=path_to_dir_images)
all_species = whale_and_dolphin.getAllSpecies()
metadata = {}
stats = pd.DataFrame(columns=['num_of_images', 'num_of_individuals'], index=all_species)
for species in all_species:
metadata[species] = whale_and_dolphin.sliceMetadata(query='species == @species')
num_images = len(metadata[species])
individual_ids = whale_and_dolphin.getAllIndividualIDs(metadata[species])
num_individuals = len(individual_ids)
stats.loc[species] = [num_images, num_individuals]
stats.loc['total'] = [stats['num_of_images'].sum(), stats['num_of_individuals'].sum()]
species = 'melon_headed_whale'
individual_ids = whale_and_dolphin.getAllIndividualIDs(metadata[species])
stats = pd.DataFrame(columns=['num_of_images'], index=individual_ids)
for individual_id in individual_ids:
metadata_individual = metadata[species].query('individual_id == @individual_id').reset_index(drop=True)
num_images = len(metadata_individual)
stats.loc[individual_id] = [num_images]
stats.loc['total'] = [stats['num_of_images'].sum()]
path_to_metadata = '%s' % path_to_test_metadata
path_to_dir_images = '%s/test_images' % path_to_inputs
whale_and_dolphin = WhaleAndDolphin(path_to_metadata=path_to_metadata, path_to_dir_images=path_to_dir_images)
all_species = whale_and_dolphin.getAllSpecies()
print('Number of images:')
print()
print('species, num_of_images')
metadata = {}
for species in all_species:
metadata[species] = whale_and_dolphin.sliceMetadata(query='species == @species')
num_images = len(metadata[species])
print('%s, %d' % (species, num_images)) | code |
88086475/cell_16 | [
"text_plain_output_1.png"
] | !ls {path_to_inputs}/train_images | head | code |
88086475/cell_17 | [
"text_plain_output_1.png"
] | !ls {path_to_inputs}/test_images | head | code |
88086475/cell_14 | [
"text_plain_output_1.png"
] | !head {path_to_inputs}/train.csv | code |
88086475/cell_53 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir_images = path_to_dir_images
self._metadata = pd.read_csv(path_to_metadata)
def getAllSpecies(self):
return self._metadata["species"].unique()
def sliceMetadata(self, query):
return self._metadata.query(query).reset_index(drop=True)
def getAllIndividualIDs(self, metadata):
return metadata["individual_id"].unique()
def showImagesTile(self, metadata, num_cols=3):
num_rows = len(metadata) // num_cols + 1
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
for row in metadata.itertuples():
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, row.Index + 1)
ax.set_title(title)
plt.imshow(image)
plt.show()
plt.clf()
plt.close()
def getImage(self, metadata_row):
title = "%s (%s)" % (metadata_row.individual_id, \
metadata_row.species)
path_to_image = self._pathToImage(
self._path_to_dir_images,
file_name=metadata_row.image
)
image = Image.open(path_to_image)
return title, image
def _pathToImage(self, path_to_dir_images, file_name):
return "%s/%s" % (path_to_dir_images, file_name)
def showIndividualImagesTile(self, metadata, num_cols=3, num_individuals=10):
metadata_sorted = \
metadata.sort_values(by=["individual_id"]).reset_index(drop=True)
num_rows = num_cols * num_individuals
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
num_images = 0
individual_id_prev = ""
i_row, i_col = 0, 0
for row in metadata_sorted.itertuples():
if row.individual_id != individual_id_prev:
# Moves to next row, if different individuals is found.
i_row += 1
i_col = 1
i = (i_row - 1) * num_cols + i_col
individual_id_prev = row.individual_id
if i_row > num_individuals:
break
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print("New individuals is found!!")
#print("> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
elif i_col < num_cols:
# Moves to next column, if the number of images for same individuals is less than num_cols.
i_col += 1
i = (i_row - 1) * num_cols + i_col
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print(">> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
else:
#print("< i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
continue
plt.show()
plt.clf()
plt.close()
path_to_metadata = '%s/train.csv' % path_to_inputs
path_to_dir_images = '%s/train_images' % path_to_inputs
whale_and_dolphin = WhaleAndDolphin(path_to_metadata=path_to_metadata, path_to_dir_images=path_to_dir_images)
all_species = whale_and_dolphin.getAllSpecies()
metadata = {}
stats = pd.DataFrame(columns=['num_of_images', 'num_of_individuals'], index=all_species)
for species in all_species:
metadata[species] = whale_and_dolphin.sliceMetadata(query='species == @species')
num_images = len(metadata[species])
individual_ids = whale_and_dolphin.getAllIndividualIDs(metadata[species])
num_individuals = len(individual_ids)
stats.loc[species] = [num_images, num_individuals]
stats.loc['total'] = [stats['num_of_images'].sum(), stats['num_of_individuals'].sum()]
species = 'melon_headed_whale'
individual_ids = whale_and_dolphin.getAllIndividualIDs(metadata[species])
stats = pd.DataFrame(columns=['num_of_images'], index=individual_ids)
for individual_id in individual_ids:
metadata_individual = metadata[species].query('individual_id == @individual_id').reset_index(drop=True)
num_images = len(metadata_individual)
stats.loc[individual_id] = [num_images]
stats.loc['total'] = [stats['num_of_images'].sum()]
path_to_metadata = '%s' % path_to_test_metadata
path_to_dir_images = '%s/test_images' % path_to_inputs
whale_and_dolphin = WhaleAndDolphin(path_to_metadata=path_to_metadata, path_to_dir_images=path_to_dir_images)
all_species = whale_and_dolphin.getAllSpecies()
metadata = {}
for species in all_species:
metadata[species] = whale_and_dolphin.sliceMetadata(query='species == @species')
num_images = len(metadata[species])
num_images = 100
for species in all_species:
print('Images for %s :' % species)
whale_and_dolphin.showImagesTile(metadata=metadata[species][:num_images], num_cols=4)
print() | code |
88086475/cell_37 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import pandas as pd
# Defines the class to load metadata and images and to process those.
class WhaleAndDolphin():
def __init__(self, path_to_metadata, path_to_dir_images):
self._path_to_metadata = path_to_metadata
self._path_to_dir_images = path_to_dir_images
self._metadata = pd.read_csv(path_to_metadata)
def getAllSpecies(self):
return self._metadata["species"].unique()
def sliceMetadata(self, query):
return self._metadata.query(query).reset_index(drop=True)
def getAllIndividualIDs(self, metadata):
return metadata["individual_id"].unique()
def showImagesTile(self, metadata, num_cols=3):
num_rows = len(metadata) // num_cols + 1
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
for row in metadata.itertuples():
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, row.Index + 1)
ax.set_title(title)
plt.imshow(image)
plt.show()
plt.clf()
plt.close()
def getImage(self, metadata_row):
title = "%s (%s)" % (metadata_row.individual_id, \
metadata_row.species)
path_to_image = self._pathToImage(
self._path_to_dir_images,
file_name=metadata_row.image
)
image = Image.open(path_to_image)
return title, image
def _pathToImage(self, path_to_dir_images, file_name):
return "%s/%s" % (path_to_dir_images, file_name)
def showIndividualImagesTile(self, metadata, num_cols=3, num_individuals=10):
metadata_sorted = \
metadata.sort_values(by=["individual_id"]).reset_index(drop=True)
num_rows = num_cols * num_individuals
fig = plt.figure(figsize=(6.4 * num_cols, 4.8 * num_rows))
num_images = 0
individual_id_prev = ""
i_row, i_col = 0, 0
for row in metadata_sorted.itertuples():
if row.individual_id != individual_id_prev:
# Moves to next row, if different individuals is found.
i_row += 1
i_col = 1
i = (i_row - 1) * num_cols + i_col
individual_id_prev = row.individual_id
if i_row > num_individuals:
break
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print("New individuals is found!!")
#print("> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
elif i_col < num_cols:
# Moves to next column, if the number of images for same individuals is less than num_cols.
i_col += 1
i = (i_row - 1) * num_cols + i_col
# Shows image.
title, image = self.getImage(row)
ax = fig.add_subplot(num_rows, num_cols, i)
ax.set_title(title)
plt.imshow(image)
#print(">> i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
else:
#print("< i_row, i_col, title, id = %d, %d, %s, %s" % (i_row, i_col, title, row.individual_id)) # for DEBUG
continue
plt.show()
plt.clf()
plt.close()
path_to_metadata = '%s/train.csv' % path_to_inputs
path_to_dir_images = '%s/train_images' % path_to_inputs
whale_and_dolphin = WhaleAndDolphin(path_to_metadata=path_to_metadata, path_to_dir_images=path_to_dir_images)
all_species = whale_and_dolphin.getAllSpecies()
metadata = {}
stats = pd.DataFrame(columns=['num_of_images', 'num_of_individuals'], index=all_species)
for species in all_species:
metadata[species] = whale_and_dolphin.sliceMetadata(query='species == @species')
num_images = len(metadata[species])
individual_ids = whale_and_dolphin.getAllIndividualIDs(metadata[species])
num_individuals = len(individual_ids)
stats.loc[species] = [num_images, num_individuals]
stats.loc['total'] = [stats['num_of_images'].sum(), stats['num_of_individuals'].sum()]
print('Number of images/individuals for each species:')
stats | code |
17130551/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ccle.txt/CCLE.txt', index_col=0)
from ast import literal_eval as make_tuple
cols = df.columns.tolist()
new_cols = [make_tuple(x) for x in cols]
df.columns = new_cols
df.shape | code |
17130551/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17130551/cell_8 | [
"text_plain_output_1.png"
] | from clustergrammer2 import net
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ccle.txt/CCLE.txt', index_col=0)
from ast import literal_eval as make_tuple
cols = df.columns.tolist()
new_cols = [make_tuple(x) for x in cols]
df.columns = new_cols
df.shape
net.load_df(df.round(2))
net.filter_N_top(inst_rc='row', N_top=1000, rank_type='var')
net.widget() | code |
17130551/cell_10 | [
"text_plain_output_1.png"
] | from clustergrammer2 import net
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ccle.txt/CCLE.txt', index_col=0)
from ast import literal_eval as make_tuple
cols = df.columns.tolist()
new_cols = [make_tuple(x) for x in cols]
df.columns = new_cols
df.shape
net.load_df(df.round(2))
net.filter_N_top(inst_rc='row', N_top=1000, rank_type='var')
net.widget()
net.load_df(df)
net.filter_N_top(inst_rc='row', N_top=1000, rank_type='var')
net.normalize(axis='row', norm_type='zscore')
df = net.export_df().round(2)
net.load_df(df)
net.widget() | code |
17130551/cell_5 | [
"text_plain_output_1.png"
] | from clustergrammer2 import net
show_widget = False
from clustergrammer2 import net
if show_widget == False:
print('\n-----------------------------------------------------')
print('>>> <<<')
print('>>> Please set show_widget to True to see widgets <<<')
print('>>> <<<')
print('-----------------------------------------------------\n')
delattr(net, 'widget_class') | code |
1005893/cell_6 | [
"text_plain_output_1.png"
] | from keras.utils.np_utils import to_categorical
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tflearn
df_trn = pd.read_csv('../input/train.csv')
df_tst = pd.read_csv('../input/test.csv')
x_trn = df_trn.ix[:, 1:].values
y_trn = df_trn.ix[:, 0].values
y_trn_cat = to_categorical(y_trn)
tf.reset_default_graph()
net = tflearn.input_data([None, 784])
net = tflearn.fully_connected(net, 256, activation='ReLU')
net = tflearn.fully_connected(net, 128, activation='ReLU')
net = tflearn.fully_connected(net, 64, activation='ReLU')
net = tflearn.fully_connected(net, 10, activation='softmax')
net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1, loss='categorical_crossentropy')
model = tflearn.DNN(net)
model.fit(x_trn, y_trn_cat, validation_set=0, show_metric=True, batch_size=1000, n_epoch=100)
np.argmax(model.predict(df_tst), 1)[0:100] | code |
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