我正在尝试在 PyCharm 中运行 Jupyter 脚本。该脚本需要 +- 2 小时才能运行。当我第一次运行它时，我的内存用完了，我退出了 pyCharm。我从我的 PC 中删除了一些文件并再次启动了 PyCharm。当我打开我的脚本并再次运行代码时，它给了我一个错误：突然它不再识别我的类“Progressbar”，而它在我第一次运行脚本时识别它。我以前没有这个错误。有谁知道这里发生了什么？
这是脚本：
import sys
import collections
import itertools
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import mode
from scipy.spatial.distance import squareform
import pandas as p
plt.style.use('bmh')
%matplotlib inline
try:
from IPython.display import clear_output
have_ipython = True
except ImportError:
have_ipython = False
class KnnDtw(object):
"""K-nearest neighbor classifier using dynamic time warping
as the distance measure between pairs of time series arrays
Arguments
---------
n_neighbors : int, optional (default = 5)
Number of neighbors to use by default for KNN
max_warping_window : int, optional (default = infinity)
Maximum warping window allowed by the DTW dynamic
programming function
subsample_step : int, optional (default = 1)
Step size for the timeseries array. By setting subsample_step = 2,
the timeseries length will be reduced by 50% because every second
item is skipped. Implemented by x[:, ::subsample_step]
"""
def __init__(self, n_neighbors=5, max_warping_window=10000, subsample_step=1):
self.n_neighbors = n_neighbors
self.max_warping_window = max_warping_window
self.subsample_step = subsample_step
def fit(self, x, l):
"""Fit the model using x as training data and l as class labels
Arguments
---------
x : array of shape [n_samples, n_timepoints]
Training data set for input into KNN classifer
l : array of shape [n_samples]
Training labels for input into KNN classifier
"""
self.x = x
self.l = l
def _dtw_distance(self, ts_a, ts_b, d = lambda x,y: abs(x-y)):
"""Returns the DTW similarity distance between two 2-D
timeseries numpy arrays.
Arguments
---------
ts_a, ts_b : array of shape [n_samples, n_timepoints]
Two arrays containing n_samples of timeseries data
whose DTW distance between each sample of A and B
will be compared
d : DistanceMetric object (default = abs(x-y))
the distance measure used for A_i - B_j in the
DTW dynamic programming function
Returns
-------
DTW distance between A and B
"""
# Create cost matrix via broadcasting with large int
ts_a, ts_b = np.array(ts_a), np.array(ts_b)
M, N = len(ts_a), len(ts_b)
cost = sys.maxsize * np.ones((M, N))
# Initialize the first row and column
cost[0, 0] = d(ts_a[0], ts_b[0])
for i in range(1, M):
cost[i, 0] = cost[i-1, 0] + d(ts_a[i], ts_b[0])
for j in range(1, N):
cost[0, j] = cost[0, j-1] + d(ts_a[0], ts_b[j])
# Populate rest of cost matrix within window
for i in range(1, M):
for j in range(max(1, i - self.max_warping_window),
min(N, i + self.max_warping_window)):
choices = cost[i - 1, j - 1], cost[i, j-1], cost[i-1, j]
cost[i, j] = min(choices) + d(ts_a[i], ts_b[j])
# Return DTW distance given window
return cost[-1, -1]
def _dist_matrix(self, x, y):
"""Computes the M x N distance matrix between the training
dataset and testing dataset (y) using the DTW distance measure
Arguments
---------
x : array of shape [n_samples, n_timepoints]
y : array of shape [n_samples, n_timepoints]
Returns
-------
Distance matrix between each item of x and y with
shape [training_n_samples, testing_n_samples]
"""
# Compute the distance matrix
dm_count = 0
# Compute condensed distance matrix (upper triangle) of pairwise dtw distances
# when x and y are the same array
if(np.array_equal(x, y)):
x_s = np.shape(x)
dm = np.zeros((x_s[0] * (x_s[0] - 1)) // 2, dtype=np.double)
p = ProgressBar(np.shape(dm)[0])
for i in range(0, x_s[0] - 1):
for j in range(i + 1, x_s[0]):
dm[dm_count] = self._dtw_distance(x[i, ::self.subsample_step],
y[j, ::self.subsample_step])
dm_count += 1
p.animate(dm_count)
# Convert to squareform
dm = squareform(dm)
return dm
# Compute full distance matrix of dtw distnces between x and y
else:
x_s = np.shape(x)
y_s = np.shape(y)
dm = np.zeros((x_s[0], y_s[0]))
dm_size = x_s[0]*y_s[0]
p = ProgressBar(dm_size)
for i in range(0, x_s[0]):
for j in range(0, y_s[0]):
dm[i, j] = self._dtw_distance(x[i, ::self.subsample_step],
y[j, ::self.subsample_step])
# Update progress bar
dm_count += 1
p.animate(dm_count)
return dm
def predict(self, x):
"""Predict the class labels or probability estimates for
the provided data
Arguments
---------
x : array of shape [n_samples, n_timepoints]
Array containing the testing data set to be classified
Returns
-------
2 arrays representing:
(1) the predicted class labels
(2) the knn label count probability
"""
dm = self._dist_matrix(x, self.x)
# Identify the k nearest neighbors
knn_idx = dm.argsort()[:, :self.n_neighbors]
# Identify k nearest labels
knn_labels = self.l[knn_idx]
# Model Label
mode_data = mode(knn_labels, axis=1)
mode_label = mode_data[0]
mode_proba = mode_data[1]/self.n_neighbors
return mode_label.ravel(), mode_proba.ravel()
class ProgressBar:
"""This progress bar was taken from PYMC
"""
def __init__(self, iterations):
self.iterations = iterations
self.prog_bar = '[]'
self.fill_char = '*'
self.width = 40
self.__update_amount(0)
if have_ipython:
self.animate = self.animate_ipython
else:
self.animate = self.animate_noipython
def animate_ipython(self, iter):
print ('\r', self,
sys.stdout.flush())
self.update_iteration(iter + 1)
def update_iteration(self, elapsed_iter):
self.__update_amount((elapsed_iter / float(self.iterations)) * 100.0)
self.prog_bar += ' %d of %s complete' % (elapsed_iter, self.iterations)
def __update_amount(self, new_amount):
percent_done = int(round((new_amount / 100.0) * 100.0))
all_full = self.width - 2
num_hashes = int(round((percent_done / 100.0) * all_full))
self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'
pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
pct_string = '%d%%' % percent_done
self.prog_bar = self.prog_bar[0:pct_place] + \
(pct_string + self.prog_bar[pct_place + len(pct_string):])
def __str__(self):
return str(self.prog_bar)
time = np.linspace(0,20,1000)
amplitude_a = 5*np.sin(time)
amplitude_b = 3*np.sin(time + 1)
m = KnnDtw()
distance = m._dtw_distance(amplitude_a, amplitude_b)
fig = plt.figure(figsize=(12,4))
_ = plt.plot(time, amplitude_a, label='A')
_ = plt.plot(time, amplitude_b, label='B')
_ = plt.title('DTW distance between A and B is %.2f' % distance)
_ = plt.ylabel('Amplitude')
_ = plt.xlabel('Time')
_ = plt.legend()
#m._dist_matrix(np.random.random((4,50)), np.random.random((4,50)))
# Import the HAR dataset
x_train_file = open('UCI HAR Dataset/train/X_train.txt', 'r')
y_train_file = open('UCI HAR Dataset/train/y_train.txt', 'r')
x_test_file = open('UCI HAR Dataset/test/X_test.txt', 'r')
y_test_file = open('UCI HAR Dataset/test/y_test.txt', 'r')
# Create empty lists
x_train = []
y_train = []
x_test = []
y_test = []
# Mapping table for classes
labels = {1:'WALKING', 2:'WALKING UPSTAIRS', 3:'WALKING DOWNSTAIRS',
4:'SITTING', 5:'STANDING', 6:'LAYING'}
# Loop through datasets
for x in x_train_file:
x_train.append([float(ts) for ts in x.split()])
for y in y_train_file:
y_train.append(int(y.rstrip('\n')))
for x in x_test_file:
x_test.append([float(ts) for ts in x.split()])
for y in y_test_file:
y_test.append(int(y.rstrip('\n')))
# Convert to numpy for efficiency
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)
m = KnnDtw(n_neighbors=1, max_warping_window=10)
m.fit(x_train[::10], y_train[::10])
label, proba = m.predict(x_test[::10])
from sklearn.metrics import classification_report, confusion_matrix
# print(classification_report(label, y_test[::10], target_names=[l for l in labels.values()]))
conf_mat = confusion_matrix(label, y_test[::10])
fig = plt.figure(figsize=(6,6))
width = np.shape(conf_mat)[1]
height = np.shape(conf_mat)[0]
res = plt.imshow(np.array(conf_mat), cmap=plt.cm.summer, interpolation='nearest')
for i, row in enumerate(conf_mat):
for j, c in enumerate(row):
if c>0:
plt.text(j-.2, i+.1, c, fontsize=16)
cb = fig.colorbar(res)
plt.title('Confusion Matrix')
_ = plt.xticks(range(6), [l for l in labels.values()], rotation=90)
_ = plt.yticks(range(6), [l for l in labels.values()])
import time
time_taken = []
windows = [1,2,5,10,50,100,500,1000,5000]
for w in windows:
begin = time.time()
t = KnnDtw(n_neighbors=1, max_warping_window=w)
t.fit(x_train[:20], y_train[:20])
label, proba = t.predict(x_test[:20])
end = time.time()
time_taken.append(end - begin)
fig = plt.figure(figsize=(12,5))
_ = plt.plot(windows, [t/400. for t in time_taken], lw=4)
plt.title('DTW Execution Time with \nvarying Max Warping Window')
plt.ylabel('Execution Time (seconds)')
plt.xlabel('Max Warping Window')
plt.xscale('log')
错误代码：
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-3-4c142a5156b6> in <module>
188 return mode_label.ravel(), mode_proba.ravel()
189
--> 190 class ProgressBar:
191 """This progress bar was taken from PYMC
192 """
<ipython-input-3-4c142a5156b6> in ProgressBar()
279 m = KnnDtw(n_neighbors=1, max_warping_window=10)
280 m.fit(x_train[::10], y_train[::10])
--> 281 label, proba = m.predict(x_test[::10])
282
283 from sklearn.metrics import classification_report, confusion_matrix
<ipython-input-3-4c142a5156b6> in predict(self, x)
173 """
174
--> 175 dm = self._dist_matrix(x, self.x)
176
177 # Identify the k nearest neighbors
<ipython-input-3-4c142a5156b6> in _dist_matrix(self, x, y)
145 dm_size = x_s[0]*y_s[0]
146
--> 147 p = ProgressBar(dm_size)
148
149 for i in range(0, x_s[0]):
NameError: name 'ProgressBar' is not defined