While performing time series weather prediction from tensorflow taken from here https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/structured_data/time_series.ipynb#scrollTo=EN4U1fcMiTYs, while using this code snippet to find the performance in terms of MSE:
x = np.arange(len(performance))
width = 0.3
metric_name = 'mean_absolute_error'
metric_index = lstm_model.metrics_names.index('mean_absolute_error')
I get an error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-80-462945e7d3dd> in <module>
2 width = 0.3
3 metric_name = 'mean_absolute_error'
----> 4 metric_index = lstm_model.metrics_names.index('mean_absolute_error')
5 val_mae = [v[metric_index] for v in val_performance.values()]
6 test_mae = [v[metric_index] for v in performance.values()]
ValueError: 'mean_absolute_error' is not in list
How do I fix this?
I am trying to code a linear regression but I am stuck on this cell, as it returns me an error and I don´t understand how to correct it. Would aprecciate some detailed feedback as to how to change my code to avoid this
Here is the cell that raises the error
error = test_predictions - test_labels
plt.hist(error, bins = 25)
plt.xlabel("Prediction Error [MPG]")
_ = plt.ylabel("Count")
After that I Get:
/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py:6630: RuntimeWarning: All-NaN slice encountered
xmin = min(xmin, np.nanmin(xi))
/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py:6631: RuntimeWarning: All-NaN slice encountered
xmax = max(xmax, np.nanmax(xi))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-30-c4d487a4d6e3> in <module>()
1 error = test_predictions - test_labels
----> 2 plt.hist(error, bins = 25)
3 plt.xlabel("Prediction Error [MPG]")
4 _ = plt.ylabel("Count")
5 frames
<__array_function__ internals> in histogram(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/numpy/lib/histograms.py in _get_outer_edges(a, range)
322 if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
323 raise ValueError(
--> 324 "autodetected range of [{}, {}] is not finite".format(first_edge, last_edge))
325
326 # expand empty range to avoid divide by zero
ValueError: autodetected range of [nan, nan] is not finite```
In a machine learning project, suppose I have 3 cat images and 2 dog images. when I make a dataframe for the training data.
#pre processing train data
filenames = os.listdir('/content/train')
categories = []
for filename in os.listdir('/content/train'):
category = filename.split('.')[0]
if category == 'dog' :
categories.append(1) #1 for dog and 0 for cat
else :
categories.append(0)
#make a dictonary
df = pd.DataFrame(
{
'filename' : filenames,
'category':categories
}
)
It gives an error because I haven't the same amount of dog, cat images.
ValueError Traceback (most recent call last)
<ipython-input-28-2d4e2440ba41> in <module>()
12 {
13 'filename' : filenames,
---> 14 'category' : categories
15 }
16 )
3 frames
/usr/local/lib/python3.6/dist-packages/pandas/core/internals/construction.py in extract_index(data)
395 lengths = list(set(raw_lengths))
396 if len(lengths) > 1:
--> 397 raise ValueError("arrays must all be same length")
398
399 if have_dicts:
ValueError: arrays must all be same length
Is there any way to fix it without adding any image to the training dataset?
I am using an amazon dataset to do sentiment analysis. Dataset content is
https://i.stack.imgur.com/qcKZp.png
dataset con be found on:
https://www.kaggle.com/PromptCloudHQ/amazon-reviews-unlocked-mobile-phones
I am trying to remove html from Review column.
This is what I am doing. Note: dataset is assigned to df.
df_removedNoise = []
def removingHTML(text):
soup = BeautifulSoup(text, 'lxml').get_text()
return soup
def removingNoise(text):
html_removed = removingHTML(text)
return html_removed
for i in df["Reviews"]:
text = removingNoise(i)
df_removedNoise.append(text)
Even though Reviews column has object as a datatype, I am still getting an error like.
TypeError Traceback (most recent call last)
<ipython-input-83-3591f5d7a54f> in <module>
9
10 for i in df["Reviews"]:
---> 11 df_removedNoise.append(removingNoise(i))
<ipython-input-83-3591f5d7a54f> in removingNoise(text)
5
6 def removingNoise(text):
----> 7 html_removed = removingHTML(text)
8 return html_removed
9
<ipython-input-83-3591f5d7a54f> in removingHTML(text)
1 df_removedNoise = []
2 def removingHTML(text):
----> 3 soup = BeautifulSoup(text, 'lxml').get_text()
4 return soup
5
~/anaconda3/lib/python3.7/site-packages/bs4/__init__.py in __init__(self, markup, features, builder, parse_only, from_encoding, exclude_encodings, **kwargs)
244 if hasattr(markup, 'read'): # It's a file-type object.
245 markup = markup.read()
--> 246 elif len(markup) <= 256 and (
247 (isinstance(markup, bytes) and not b'<' in markup)
248 or (isinstance(markup, str) and not '<' in markup)
TypeError: object of type 'float' has no len()
Any help will be appreciated!
Check for NaN with df[df['Reviews'].isnull()], if you find any try to dropna first
I'm plotting subsets of a dataframe, and one subset happens to have only one row. This is the only reason I can think of for why it's causing problems. This is what it looks like:
problem_dataframe = prob_df[prob_df['Date']==7]
problem_dataframe.head()
I try to do:
sns.distplot(problem_dataframe['floatTime'])
But I get the error:
TypeError: len() of unsized object
Would someone please tell me what's causing this and how to work around it?
The TypeError is resolved by setting bins=1.
But that uncovers a different error, ValueError: x must be 1D or 2D, which gets triggered by an internal function in Matplotlib's hist(), called _normalize_input():
import pandas as pd
import seaborn as sns
df = pd.DataFrame(['Tue','Feb',7,'15:37:58',2017,15.6196]).T
df.columns = ['Day','Month','Date','Time','Year','floatTime']
sns.distplot(df.floatTime, bins=1)
Output:
ValueError Traceback (most recent call last)
<ipython-input-25-858df405d200> in <module>()
6 df.columns = ['Day','Month','Date','Time','Year','floatTime']
7 df.floatTime.values.astype(float)
----> 8 sns.distplot(df.floatTime, bins=1)
/home/andrew/anaconda3/lib/python3.6/site-packages/seaborn/distributions.py in distplot(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax)
213 hist_color = hist_kws.pop("color", color)
214 ax.hist(a, bins, orientation=orientation,
--> 215 color=hist_color, **hist_kws)
216 if hist_color != color:
217 hist_kws["color"] = hist_color
/home/andrew/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
1890 warnings.warn(msg % (label_namer, func.__name__),
1891 RuntimeWarning, stacklevel=2)
-> 1892 return func(ax, *args, **kwargs)
1893 pre_doc = inner.__doc__
1894 if pre_doc is None:
/home/andrew/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py in hist(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
6141 x = np.array([[]])
6142 else:
-> 6143 x = _normalize_input(x, 'x')
6144 nx = len(x) # number of datasets
6145
/home/andrew/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py in _normalize_input(inp, ename)
6080 else:
6081 raise ValueError(
-> 6082 "{ename} must be 1D or 2D".format(ename=ename))
6083 if inp.shape[1] < inp.shape[0]:
6084 warnings.warn(
ValueError: x must be 1D or 2D
_normalize_input() was removed from Matplotlib (it looks like sometime last year), so I guess Seaborn is referring to an older version under the hood.
You can see _normalize_input() in this old commit:
def _normalize_input(inp, ename='input'):
"""Normalize 1 or 2d input into list of np.ndarray or
a single 2D np.ndarray.
Parameters
----------
inp : iterable
ename : str, optional
Name to use in ValueError if `inp` can not be normalized
"""
if (isinstance(x, np.ndarray) or
not iterable(cbook.safe_first_element(inp))):
# TODO: support masked arrays;
inp = np.asarray(inp)
if inp.ndim == 2:
# 2-D input with columns as datasets; switch to rows
inp = inp.T
elif inp.ndim == 1:
# new view, single row
inp = inp.reshape(1, inp.shape[0])
else:
raise ValueError(
"{ename} must be 1D or 2D".format(ename=ename))
...
I can't figure out why inp.ndim!=1, though. Performing the same np.asarray().ndim on the input returns 1 as expected:
np.asarray(df.floatTime).ndim # 1
So you're facing a few obstacles if you want to make a single-valued input work with sns.distplot().
Suggested Workaround
Check for a single-element df.floatTime, and if that's the case, just use plt.hist() instead (which is what distplot goes to anyway, along with KDE):
plt.hist(df.floatTime)