Keras Tutorial Deep Learning In Python Walkthrough

Walkthrough of datacamp tutorial of : (https://www.datacamp.com/community/tutorials/deep-learning-python)

Load the data

import pandas as pd

# Read in white wine data 
white = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv", sep=';')

# Read in red wine data 
red = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv", sep=';')

white.head()

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	7.0	0.27	0.36	20.7	0.045	45.0	170.0	1.0010	3.00	0.45	8.8	6
1	6.3	0.30	0.34	1.6	0.049	14.0	132.0	0.9940	3.30	0.49	9.5	6
2	8.1	0.28	0.40	6.9	0.050	30.0	97.0	0.9951	3.26	0.44	10.1	6
3	7.2	0.23	0.32	8.5	0.058	47.0	186.0	0.9956	3.19	0.40	9.9	6
4	7.2	0.23	0.32	8.5	0.058	47.0	186.0	0.9956	3.19	0.40	9.9	6

red.head()

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5
1	7.8	0.88	0.00	2.6	0.098	25.0	67.0	0.9968	3.20	0.68	9.8	5
2	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.9970	3.26	0.65	9.8	5
3	11.2	0.28	0.56	1.9	0.075	17.0	60.0	0.9980	3.16	0.58	9.8	6
4	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5

Describe and Familiarize your Data

white.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4898 entries, 0 to 4897
Data columns (total 12 columns):
fixed acidity           4898 non-null float64
volatile acidity        4898 non-null float64
citric acid             4898 non-null float64
residual sugar          4898 non-null float64
chlorides               4898 non-null float64
free sulfur dioxide     4898 non-null float64
total sulfur dioxide    4898 non-null float64
density                 4898 non-null float64
pH                      4898 non-null float64
sulphates               4898 non-null float64
alcohol                 4898 non-null float64
quality                 4898 non-null int64
dtypes: float64(11), int64(1)
memory usage: 459.3 KB

red.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1599 entries, 0 to 1598
Data columns (total 12 columns):
fixed acidity           1599 non-null float64
volatile acidity        1599 non-null float64
citric acid             1599 non-null float64
residual sugar          1599 non-null float64
chlorides               1599 non-null float64
free sulfur dioxide     1599 non-null float64
total sulfur dioxide    1599 non-null float64
density                 1599 non-null float64
pH                      1599 non-null float64
sulphates               1599 non-null float64
alcohol                 1599 non-null float64
quality                 1599 non-null int64
dtypes: float64(11), int64(1)
memory usage: 150.0 KB

white.sample()

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
2017	9.0	0.55	0.3	8.1	0.026	14.0	71.0	0.993	2.94	0.36	11.8	5

red.sample()

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
1146	7.8	0.5	0.12	1.8	0.178	6.0	21.0	0.996	3.28	0.87	9.8	6

white.describe()

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
count	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000	4898.000000
mean	6.854788	0.278241	0.334192	6.391415	0.045772	35.308085	138.360657	0.994027	3.188267	0.489847	10.514267	5.877909
std	0.843868	0.100795	0.121020	5.072058	0.021848	17.007137	42.498065	0.002991	0.151001	0.114126	1.230621	0.885639
min	3.800000	0.080000	0.000000	0.600000	0.009000	2.000000	9.000000	0.987110	2.720000	0.220000	8.000000	3.000000
25%	6.300000	0.210000	0.270000	1.700000	0.036000	23.000000	108.000000	0.991723	3.090000	0.410000	9.500000	5.000000
50%	6.800000	0.260000	0.320000	5.200000	0.043000	34.000000	134.000000	0.993740	3.180000	0.470000	10.400000	6.000000
75%	7.300000	0.320000	0.390000	9.900000	0.050000	46.000000	167.000000	0.996100	3.280000	0.550000	11.400000	6.000000
max	14.200000	1.100000	1.660000	65.800000	0.346000	289.000000	440.000000	1.038980	3.820000	1.080000	14.200000	9.000000

red.describe()

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
count	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000	1599.000000
mean	8.319637	0.527821	0.270976	2.538806	0.087467	15.874922	46.467792	0.996747	3.311113	0.658149	10.422983	5.636023
std	1.741096	0.179060	0.194801	1.409928	0.047065	10.460157	32.895324	0.001887	0.154386	0.169507	1.065668	0.807569
min	4.600000	0.120000	0.000000	0.900000	0.012000	1.000000	6.000000	0.990070	2.740000	0.330000	8.400000	3.000000
25%	7.100000	0.390000	0.090000	1.900000	0.070000	7.000000	22.000000	0.995600	3.210000	0.550000	9.500000	5.000000
50%	7.900000	0.520000	0.260000	2.200000	0.079000	14.000000	38.000000	0.996750	3.310000	0.620000	10.200000	6.000000
75%	9.200000	0.640000	0.420000	2.600000	0.090000	21.000000	62.000000	0.997835	3.400000	0.730000	11.100000	6.000000
max	15.900000	1.580000	1.000000	15.500000	0.611000	72.000000	289.000000	1.003690	4.010000	2.000000	14.900000	8.000000

import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (20,10)
import seaborn as sns

sns.set_style("whitegrid")
sns.boxplot(data = white) 

<matplotlib.axes._subplots.AxesSubplot at 0x7fa90017c5f8>

sns.boxplot(data = red) 

<matplotlib.axes._subplots.AxesSubplot at 0x7fa8ffc03f98>

_config.yml

Preprocess Data

# Add `type` column to `red` with value 1
red['type'] = 1

# Add `type` column to `white` with value 0
white['type'] = 0

# Append `white` to `red`
wines = red.append(white, ignore_index=True)

wines.sample()

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality	type
1182	10.2	0.4	0.4	2.5	0.068	41.0	54.0	0.99754	3.38	0.86	10.5	6	1

import seaborn as sns
corr = wines.corr()
sns.heatmap(corr, 
            xticklabels=corr.columns.values,
            yticklabels=corr.columns.values)
plt.show()

_config.yml

Split Train and Test

from sklearn.model_selection import train_test_split
import numpy as np

# Specify the data 
X=wines.iloc[:,0:-1]  # 12 dimensions

# Specify the target labels and flatten the array 
y=np.ravel(wines.type)

# Split the data up in train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

X_train.head()

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
1700	7.1	0.12	0.32	9.6	0.054	64.0	162.0	0.99620	3.40	0.41	9.4	5
5199	6.8	0.12	0.30	12.9	0.049	32.0	88.0	0.99654	3.20	0.35	9.9	6
3340	7.7	0.38	0.40	2.0	0.038	28.0	152.0	0.99060	3.18	0.32	12.9	6
86	8.6	0.49	0.28	1.9	0.110	20.0	136.0	0.99720	2.93	1.95	9.9	6
5587	6.1	0.20	0.17	1.6	0.048	46.0	129.0	0.99100	3.30	0.43	11.4	6

X_train.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 4352 entries, 1700 to 860
Data columns (total 12 columns):
fixed acidity           4352 non-null float64
volatile acidity        4352 non-null float64
citric acid             4352 non-null float64
residual sugar          4352 non-null float64
chlorides               4352 non-null float64
free sulfur dioxide     4352 non-null float64
total sulfur dioxide    4352 non-null float64
density                 4352 non-null float64
pH                      4352 non-null float64
sulphates               4352 non-null float64
alcohol                 4352 non-null float64
quality                 4352 non-null int64
dtypes: float64(11), int64(1)
memory usage: 442.0 KB

X_test.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2145 entries, 3103 to 2683
Data columns (total 12 columns):
fixed acidity           2145 non-null float64
volatile acidity        2145 non-null float64
citric acid             2145 non-null float64
residual sugar          2145 non-null float64
chlorides               2145 non-null float64
free sulfur dioxide     2145 non-null float64
total sulfur dioxide    2145 non-null float64
density                 2145 non-null float64
pH                      2145 non-null float64
sulphates               2145 non-null float64
alcohol                 2145 non-null float64
quality                 2145 non-null int64
dtypes: float64(11), int64(1)
memory usage: 217.9 KB

Standardize The Data

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler().fit(X_train)

X_train = scaler.transform(X_train)  # scaler should be fitted to train only not the entire set

X_test = scaler.transform(X_test)

sns.boxplot(data = X_train) 

<matplotlib.axes._subplots.AxesSubplot at 0x7fa8fedd4d68>

_config.yml

sns.boxplot(data = X_test)

<matplotlib.axes._subplots.AxesSubplot at 0x7fa8fe4a4940>

_config.yml

MLP Data Model

import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Initialize the constructor
model = Sequential()

# Add an input layer 
model.add(Dense(12, activation='relu', input_shape=(12,)))

# Add one hidden layer 
model.add(Dense(8, activation='relu'))

# Add an output layer 
model.add(Dense(1, activation='sigmoid'))

X_train.shape

(4352, 12)

model.input_shape

(None, 12)

model.output_shape

(None, 1)

model.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 12)                156       
_________________________________________________________________
dense_1 (Dense)              (None, 8)                 104       
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 9         
=================================================================
Total params: 269
Trainable params: 269
Non-trainable params: 0
_________________________________________________________________

model.get_config()

{'name': 'sequential',
 'layers': [{'class_name': 'Dense',
   'config': {'name': 'dense',
    'trainable': True,
    'batch_input_shape': (None, 12),
    'dtype': 'float32',
    'units': 12,
    'activation': 'relu',
    'use_bias': True,
    'kernel_initializer': {'class_name': 'GlorotUniform',
     'config': {'seed': None}},
    'bias_initializer': {'class_name': 'Zeros', 'config': {}},
    'kernel_regularizer': None,
    'bias_regularizer': None,
    'activity_regularizer': None,
    'kernel_constraint': None,
    'bias_constraint': None}},
  {'class_name': 'Dense',
   'config': {'name': 'dense_1',
    'trainable': True,
    'dtype': 'float32',
    'units': 8,
    'activation': 'relu',
    'use_bias': True,
    'kernel_initializer': {'class_name': 'GlorotUniform',
     'config': {'seed': None}},
    'bias_initializer': {'class_name': 'Zeros', 'config': {}},
    'kernel_regularizer': None,
    'bias_regularizer': None,
    'activity_regularizer': None,
    'kernel_constraint': None,
    'bias_constraint': None}},
  {'class_name': 'Dense',
   'config': {'name': 'dense_2',
    'trainable': True,
    'dtype': 'float32',
    'units': 1,
    'activation': 'sigmoid',
    'use_bias': True,
    'kernel_initializer': {'class_name': 'GlorotUniform',
     'config': {'seed': None}},
    'bias_initializer': {'class_name': 'Zeros', 'config': {}},
    'kernel_regularizer': None,
    'bias_regularizer': None,
    'activity_regularizer': None,
    'kernel_constraint': None,
    'bias_constraint': None}}]}

model.get_weights()

[array([[ 0.18509841, -0.1787318 , -0.1974895 ,  0.47024655, -0.22014785,
         -0.0336175 , -0.4499972 ,  0.00948608, -0.17663336, -0.13032675,
         -0.20205772,  0.48160243],
        [-0.01900232,  0.01159549,  0.37995243, -0.19818723, -0.10916328,
          0.14702225,  0.03217804, -0.25475132,  0.03949857,  0.42871344,
          0.0091548 , -0.28606224],
        [-0.22698724, -0.00181675,  0.28305113, -0.42118   , -0.4363984 ,
          0.396356  ,  0.34379554, -0.07786012, -0.20018017, -0.22760534,
         -0.41003287, -0.16024351],
        [-0.21674359,  0.13974214,  0.44481695,  0.03168631, -0.41572845,
          0.05836797, -0.40699494, -0.21628869,  0.4499054 , -0.29570246,
         -0.13334882,  0.1724205 ],
        [-0.16441262,  0.2967329 ,  0.33999276, -0.3159808 , -0.22747147,
         -0.24628317,  0.1189853 , -0.00377738,  0.4081782 , -0.08154869,
         -0.404258  , -0.3623103 ],
        [-0.26187372,  0.21067333, -0.10333037,  0.4204389 , -0.08828092,
         -0.44783163, -0.0311656 ,  0.1310252 , -0.21938753,  0.48668897,
         -0.21560037, -0.02168429],
        [-0.08842039, -0.09222972, -0.34199488,  0.0538379 , -0.11738276,
         -0.24480498,  0.2634858 , -0.16824949, -0.24320757, -0.06541574,
         -0.1200999 , -0.14858925],
        [ 0.32152343, -0.07561326, -0.42344034,  0.20426881, -0.46129727,
         -0.451545  , -0.07423818,  0.35979104, -0.45212865,  0.05693948,
          0.4395585 ,  0.02052164],
        [-0.37374985,  0.46638262,  0.40280735, -0.3428874 , -0.2306912 ,
          0.435421  ,  0.07876062,  0.36400735, -0.2985438 , -0.2733376 ,
         -0.08336091,  0.2731043 ],
        [-0.45887685, -0.42721224, -0.15539205,  0.15447009,  0.01825047,
         -0.20035589, -0.41170645,  0.28550863,  0.08846998, -0.4694103 ,
         -0.48388457, -0.35172224],
        [ 0.34384847,  0.31992245,  0.26032948,  0.31604683,  0.2847165 ,
         -0.07904387, -0.47391832, -0.48427117, -0.40004635,  0.4950372 ,
          0.1061877 , -0.4887694 ],
        [-0.07969391,  0.39160895, -0.19645143, -0.14542246, -0.49597108,
          0.4747815 , -0.11582911,  0.33142388,  0.16304743, -0.10827124,
          0.2970295 , -0.13576949]], dtype=float32),
 array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32),
 array([[-0.12573722, -0.48548222,  0.4242099 , -0.38060397,  0.16979319,
          0.0918628 ,  0.03741366,  0.3321216 ],
        [-0.01776743,  0.19143659, -0.31154966, -0.30466095,  0.48119617,
         -0.1385485 ,  0.00675309,  0.17020297],
        [-0.40346807, -0.3330045 ,  0.41555822,  0.5237893 ,  0.35933822,
          0.345599  , -0.29075652, -0.34157613],
        [-0.48182094,  0.11174613, -0.31294814,  0.04721171, -0.33019376,
          0.22745019, -0.3051402 , -0.47627673],
        [-0.47520423, -0.35167938, -0.253953  ,  0.25971293, -0.23213515,
         -0.33326215,  0.4418798 ,  0.36893743],
        [ 0.19385517,  0.27362376,  0.51028967, -0.3290528 , -0.25164944,
          0.00835991,  0.3839218 ,  0.09181195],
        [-0.49004847,  0.48177254, -0.19130912, -0.48774895,  0.5439503 ,
         -0.5147951 ,  0.14143002, -0.06817371],
        [ 0.34650046,  0.40215737, -0.45702255, -0.21625286, -0.3195373 ,
         -0.13152903,  0.00269675,  0.1602022 ],
        [-0.0268448 ,  0.23023522, -0.28147897,  0.03006667,  0.32084173,
          0.2816946 , -0.34124613, -0.42064995],
        [ 0.37168366,  0.18144089,  0.30210078,  0.05433446, -0.15761724,
         -0.42090005,  0.2879694 , -0.42921987],
        [-0.41087604, -0.01969856, -0.2089597 , -0.35769606,  0.31698042,
         -0.21026021, -0.13029772, -0.5287101 ],
        [-0.4796678 ,  0.29415208, -0.32441646, -0.2662846 , -0.12991512,
         -0.39028126, -0.06847224,  0.25392455]], dtype=float32),
 array([0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32),
 array([[ 0.43139648],
        [ 0.812376  ],
        [-0.18619502],
        [-0.5815563 ],
        [-0.2090925 ],
        [-0.11155289],
        [-0.7855999 ],
        [ 0.01194578]], dtype=float32),
 array([0.], dtype=float32)]

Training

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
                   
history = model.fit(X_train, y_train,epochs=20, batch_size=1, validation_data=(X_test, y_test), verbose=1)

Train on 4352 samples, validate on 2145 samples
Epoch 1/20
4352/4352 [==============================] - 19s 4ms/sample - loss: 0.0883 - accuracy: 0.9754 - val_loss: 0.0444 - val_accuracy: 0.9874
Epoch 2/20
4352/4352 [==============================] - 17s 4ms/sample - loss: 0.0241 - accuracy: 0.9947 - val_loss: 0.0325 - val_accuracy: 0.9944
Epoch 3/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0203 - accuracy: 0.9963 - val_loss: 0.0263 - val_accuracy: 0.9949
Epoch 4/20
4352/4352 [==============================] - 17s 4ms/sample - loss: 0.0165 - accuracy: 0.9968 - val_loss: 0.0253 - val_accuracy: 0.9958
Epoch 5/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0147 - accuracy: 0.9975 - val_loss: 0.0223 - val_accuracy: 0.9958
Epoch 6/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0132 - accuracy: 0.9970 - val_loss: 0.0253 - val_accuracy: 0.9958
Epoch 7/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0137 - accuracy: 0.9972 - val_loss: 0.0229 - val_accuracy: 0.9963
Epoch 8/20
4352/4352 [==============================] - 17s 4ms/sample - loss: 0.0110 - accuracy: 0.9982 - val_loss: 0.0223 - val_accuracy: 0.9958
Epoch 9/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0108 - accuracy: 0.9977 - val_loss: 0.0230 - val_accuracy: 0.9953
Epoch 10/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0098 - accuracy: 0.9982 - val_loss: 0.0232 - val_accuracy: 0.9958
Epoch 11/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0085 - accuracy: 0.9979 - val_loss: 0.0372 - val_accuracy: 0.9921
Epoch 12/20
4352/4352 [==============================] - 17s 4ms/sample - loss: 0.0100 - accuracy: 0.9977 - val_loss: 0.0276 - val_accuracy: 0.9949
Epoch 13/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0105 - accuracy: 0.9977 - val_loss: 0.0284 - val_accuracy: 0.9949
Epoch 14/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0081 - accuracy: 0.9984 - val_loss: 0.0246 - val_accuracy: 0.9958
Epoch 15/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0075 - accuracy: 0.9979 - val_loss: 0.0257 - val_accuracy: 0.9953
Epoch 16/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0097 - accuracy: 0.9975 - val_loss: 0.0229 - val_accuracy: 0.9953
Epoch 17/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0072 - accuracy: 0.9982 - val_loss: 0.0272 - val_accuracy: 0.9953
Epoch 18/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0074 - accuracy: 0.9986 - val_loss: 0.0263 - val_accuracy: 0.9958
Epoch 19/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0079 - accuracy: 0.9982 - val_loss: 0.0283 - val_accuracy: 0.9953
Epoch 20/20
4352/4352 [==============================] - 18s 4ms/sample - loss: 0.0067 - accuracy: 0.9986 - val_loss: 0.0279 - val_accuracy: 0.9949

print(history.history.keys())
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])

_config.yml

Evaluate Model

score = model.evaluate(X_test, y_test,verbose=1)
print(score)

2145/1 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- 0s 111us/sample - loss: 0.0139 - accuracy: 0.9949

preds = model.predict(X_test)
print(preds)

preds = np.where(preds > 0.5, 1, 0)
print(preds)

def plot_confusion_matrix(cm,
                          target_names,
                          title='Confusion matrix',
                          cmap=None,
                          normalize=True):
    """
    given a sklearn confusion matrix (cm), make a nice plot
cmap = matplotlib.cm.get_cmap('Reds')

    Arguments
    ---------
    cm:           confusion matrix from sklearn.metrics.confusion_matrix

    target_names: given classification classes such as [0, 1, 2]
                  the class names, for example: ['high', 'medium', 'low']

    title:        the text to display at the top of the matrix

    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm
                  see http://matplotlib.org/examples/color/colormaps_reference.html
                  plt.get_cmap('jet') or plt.cm.Blues

    normalize:    If False, plot the raw numbers
                  If True, plot the proportions

    Usage
    -----
    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by
                                                              # sklearn.metrics.confusion_matrix
                          normalize    = True,                # show proportions
                          target_names = y_labels_vals,       # list of names of the classes
                          title        = best_estimator_name) # title of graph

    Citiation
    ---------
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

    """
    import itertools

    accuracy = np.trace(cm) / float(np.sum(cm))
    misclass = 1 - accuracy

    if cmap is None:
        cmap = plt.get_cmap('Blues')

    plt.figure(figsize=(8, 8))
    ax = plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()

    if target_names is not None:
        tick_marks = np.arange(len(target_names))
        plt.xticks(tick_marks, target_names, rotation=45)
        plt.yticks(tick_marks, target_names)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]


    thresh = cm.max() / 1.5 if normalize else cm.max() / 2
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        if normalize:
            plt.text(j, i, "{:0.4f}".format(cm[i, j]),
                     horizontalalignment="center",
                     verticalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")
        else:
            plt.text(j, i, "{:,}".format(cm[i, j]),
                     horizontalalignment="center",
                     verticalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")


    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
    plt.show()

from sklearn.metrics import confusion_matrix

title = 'Confusion matrix: '  
sns.set(style='ticks')
target_names = ['white','red']  # white = 0, red = 1
cm =confusion_matrix(y_test, preds)
plot_confusion_matrix(cm, target_names, title=title, cmap=None, normalize=False)

_config.yml

Written on November 1, 2019