import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from deepchecks import Dataset
from deepchecks.checks import TrainTestFeatureDrift, ConfusionMatrixReport
from deepchecks.suites import full_suite

# Generate a synthetic dataset
from sklearn.datasets import make_classification

# Generate a dataset with 2 classes
X, y …

import numpy as np
import pandas as pd

datatype = [('one', 'int32'), ('two', 'int32')]

values = np.zeros(3, dtype = datatype)

currrent_index = ['Row '+str(i) for i in range(1, 4)]

df = pd.DataFrame(values, index = currrent_index)

df

import numpy as np
import matplotlib.pyplot as plt


data = [[  66386,  174296,   75131,  577908,   32015],
        [  58230,  381139,   78045,   99308,  160454],
        [  89135,   80552,  152558,  497981,  603535],
        [  78415,   81858,  150656,  193263,   69638],
        [ 139361,  331509,  343164,  781380,   52269]]

columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
rows = ['%d year' % x for x in (100, 50 …

from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook
from bokeh.models import ColumnDataSource

# Set Bokeh output to notebook
output_notebook()

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

a = tf.ones((2, 2))
b = tf.Variable(tf.zeros((2, 2)), name = 'weights')

with tf …

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

a = tf.random_uniform((4, 4), 0, 1)

a

<tf.Tensor 'random_uniform_2:0' shape=(4, 4 …

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

w = tf.Variable([.2], dtype=tf.float32)

b = tf.Variable([0.11], dtype=tf.float32)

x …

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report

X = np.array(
    [
        [1, 1], [1, 2], [2 …

import pandas as pd

url = 'https://gist.githubusercontent.com/michhar/2dfd2de0d4f8727f873422c5d959fff5/raw …

# scipy
import scipy
print('scipy: %s' % scipy.__version__)

scipy: 1.2.1

# numpy
import numpy
print('numpy: %s' % numpy.__version__)

numpy: 1.16.2

# matplotlib
import matplotlib
print('matplotlib: %s' % matplotlib.__version__)

matplotlib: 3.0.3

# pandas …