pandasで円グラフ作成

pandasでpltは直接できて便利

defaulte_fig_size = plt.rcParams["figure.figsize"]
plt.rcParams["figure.figsize"] = [12.0, 10.0]
# plt.figure()
# fig, axes = plt.subplots(nrows=4, ncols=1, )
fig = plt.figure()

ax1 = fig.add_subplot(221)
ax1.title.set_text('all member')
df_all_cluster[["count_cluster"]].plot(kind='pie', subplots=True, title='all member', ax=ax1, legend=False)

ax2 = fig.add_subplot(222)
df_2[["count_cluster"]].plot(kind='pie', subplots=True, title='ax2', ax=ax2, legend=False)
ax2.title.set_text('ax2')

ax3 = fig.add_subplot(223)
df_3[["count_cluster"]].plot(kind='pie', subplots=True,title='ax3', ax=ax3, legend=False)
ax3.title.set_text('ax3')

ax4 = fig.add_subplot(224)
df_4[["count_cluster"]].plot(kind='pie', subplots=True, title='ax4', ax=ax4, legend=False)
ax4.title.set_text('ax4')

plt.savefig('cluster.png', dpi=None)
plt.show()
plt.rcParams["figure.figsize"] = defaulte_fig_size
plt.close('all')

stackoverflow.com

note.nkmk.me

以下でも良いかも。

fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(12, 10))
# 左上
ax[0, 0].plot(x, y1)

# 右上
ax[0, 1].plot(x, y2)

# 左下
ax[1, 0].plot(x, y3)

# 右下
ax[1, 1].plot(x, y4)

qiita.com

tensflow で CNN を試す

CNN のチュートリアルをやってみた。 画像以外でも使いたい。

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

train_data = mnist.train.images # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

## cnn_model_fn から、EstimatorのSpecを受け、分類器の作成
mnist_classifier = tf.estimator.Estimator(
    model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")

## 進行状況のログ
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(
    tensors=tensors_to_log, every_n_iter=50)

## トレーニング
train_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": train_data},
    y=train_labels,
    batch_size=100,
    num_epochs=None,
    shuffle=True)
mnist_classifier.train(
    input_fn=train_input_fn,
    steps=20000,
    hooks=[logging_hook])

## テスト
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": eval_data},
    y=eval_labels,
    num_epochs=1,
    shuffle=False)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
def cnn_model_fn(features, labels, mode):
  """Model function for CNN."""
  # Input Layer
  input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])

  # Convolutional Layer #1
  conv1 = tf.layers.conv2d(
      inputs=input_layer,
      filters=32,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)

  # Pooling Layer #1
  pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

  # Convolutional Layer #2 and Pooling Layer #2
  conv2 = tf.layers.conv2d(
      inputs=pool1,
      filters=64,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)
  pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

  # Dense Layer
  pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
  dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
  dropout = tf.layers.dropout(
      inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)

  # Logits Layer
  logits = tf.layers.dense(inputs=dropout, units=10)

  predictions = {
      # Generate predictions (for PREDICT and EVAL mode)
      "classes": tf.argmax(input=logits, axis=1),
      # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
      # `logging_hook`.
      "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
  }

  if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

  # Calculate Loss (for both TRAIN and EVAL modes)
  loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)

  # Configure the Training Op (for TRAIN mode)
  if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
    train_op = optimizer.minimize(
        loss=loss,
        global_step=tf.train.get_global_step())
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

  # Add evaluation metrics (for EVAL mode)
  eval_metric_ops = {
      "accuracy": tf.metrics.accuracy(
          labels=labels, predictions=predictions["classes"])}
  return tf.estimator.EstimatorSpec(
      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

Build a Convolutional Neural Network using Estimators  |  TensorFlow

matplotlib で figureの大きさを変える方法

import math
import numpy as np
from matplotlib import pyplot
fig = pyplot.figure(figsize=(12, 4))
pi = math.pi   #mathモジュールのπを利用
 
x = np.linspace(0, 2*pi, 100)  #0から2πまでの範囲を100分割したnumpy配列
y = np.sin(x)
# adjustFigAspect(fig,aspect=.5)
ax = fig.add_subplot(111)
ax.plot(range(10),range(10))
def adjustFigAspect(fig,aspect=1):
    '''
    Adjust the subplot parameters so that the figure has the correct
    aspect ratio.
    '''
    xsize,ysize = fig.get_size_inches()
    minsize = min(xsize,ysize)
    xlim = .4*minsize/xsize
    ylim = .4*minsize/ysize
    if aspect < 1:
        xlim *= aspect
    else:
        ylim /= aspect
    fig.subplots_adjust(left=.5-xlim,
                        right=.5+xlim,
                        bottom=.5-ylim,
                        top=.5+ylim)

stackoverflow.com

stackoverflow.com

こんな感じにデフォルト設定を変えるのもあり

defaulte_fig_size = plt.rcParams["figure.figsize"]
plt.rcParams["figure.figsize"] = [6.0, 18.0]
...
plt.tight_layout()
plt.show()
plt.rcParams["figure.figsize"] = defaulte_fig_size

codeyarns.com