I am working on a project that has user reviews on products. I am using TfidfVectorizer to extract features from my dataset apart from some other features that I have extracted manually.
df = pd.read_csv('reviews.csv', header=0)
FEATURES = ['feature1', 'feature2']
reviews = df['review']
reviews = reviews.values.flatten()
vectorizer = TfidfVectorizer(min_df=1, decode_error='ignore', ngram_range=(1, 3), stop_words='english', max_features=45)
X = vectorizer.fit_transform(reviews)
idf = vectorizer.idf_
features = vectorizer.get_feature_names()
FEATURES += features
inverse = vectorizer.inverse_transform(X)
for i, row in df.iterrows():
for f in features:
df.set_value(i, f, False)
for inv in inverse[i]:
df.set_value(i, inv, True)
train_df, test_df = train_test_split(df, test_size = 0.2, random_state=700)
The above code works fine. But when I change the max_features from 45 to anything higher I get an error on tran_test_split line.
Traceback as follows:
Traceback (most recent call last):
File "analysis.py", line 120, in <module>
train_df, test_df = train_test_split(df, test_size = 0.2, random_state=700)
File "/Users/user/Tools/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py", line 1906, in train_test_split
arrays = indexable(*arrays)
File "/Users/user/Tools/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py", line 201, in indexable
check_consistent_length(*result)
File "/Users/user/Tools/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py", line 173, in check_consistent_length
uniques = np.unique([_num_samples(X) for X in arrays if X is not None])
File "/Users/user/Tools/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py", line 112, in _num_samples
'estimator %s' % x)
TypeError: Expected sequence or array-like, got estimator
I am not sure what exactly is changing when I change increase the max_features size.
Let me know if you need more data or if I have missed something
I know this is old, but I had the same issue and while the answer from #shahins works, I wanted something that would keep the dataframe object so I can have my indexing in the train/test splits.
Solution:
Rename the dataframe column fit as something (anything) else:
df = df.rename(columns = {'fit': 'fit_feature'})
Why it works:
It isn't actually the number of features that is the issue, it is one feature in particular that is causing the problem. I'm guessing you are getting the word "fit" as one of your text features (and it didn't show up with the lower max_features threshold).
Looking at the sklearn source code, it checks to make sure you are not passing an sklearn estimator by testing to see if the any of your objects have a "fit" attribute. The code is checking for the fit method of an sklearn estimator, but will also raise an exception when you have a fit column of the dataframe (remember df.fit and df['fit'] both select the "fit" column).
I had this issue and I tried something like this and it worked for me:
train_test_split(df.as_matrix(), test_size = 0.2, random_state=700)
train_test_split(x.as_matrix(), y.as_matrix(), test_size=0.2, random_state=0)
This worked for me.
I've recently migrated to a fully_connected style model that reads inputs from a queue generated from a TFRecords file. This has proven much more efficient, but I still would like to pass parameters interactively with placeholder/feed_dict.
Is there a way to use the same computation graph (say you have a model class that builds a graph in the init method) for both a feed_dict and full_connected functionality? Can you get a placeholder to receive values from a dequeue?
One possibility is to use the recently added (in TensorFlow 0.8) tf.placeholder_with_default() op, which allows you to specify a default value (typically the output of the queue/reader), and also allows you to feed values that might have different shapes.
For example, let's say your queue produces batches of 32 elements, where each elements has 784 features, to give a 32 x 784 matrix.
input_from_queue = ... # e.g. `queue.dequeue_many(32)` or `tf.train.batch(..., 32)`
# input_from_queue.get_shape() ==> (32, 784)
input = tf.placeholder_with_default(input_from_queue, shape=(None, 784))
# input.get_shape() ==> (?, 784)
# ...
train_op = ...
sess.run(train_op) # Takes examples from `queue`.
sess.run(train_op, feed_dict={input: ...}) # Takes examples from `feed_dict`.
This allows you to feed in variable-sized batches or use an input reader, as desired.
You could simply feed the output of the dequeue operation. TensorFlow would not actually dequeue any item, it would just use the value you provided. For example:
q = tf.FIFOQueue(capacity=10, dtypes=[tf.float32], shapes=[()])
v = tf.placeholder(tf.float32)
enqueue = q.enqueue([v])
dequeue = q.dequeue()
output = dequeue + 10.0
with tf.Session() as sess:
sess.run(enqueue, feed_dict={v: 1.0})
sess.run(enqueue, feed_dict={v: 2.0})
sess.run(enqueue, feed_dict={v: 3.0})
print(sess.run(output)) # 11.0
print(sess.run(output, feed_dict={dequeue: 5.0})) # 15.0
print(sess.run(output)) # 12.0
print(sess.run(output)) # 13.0
I have been collecting tweets from the past week to collect the past-7-days tweets related to "lung cancer", yesterday, I figured I needed to start collecting more fields, so I added some fields and started re-collecting the same period of Tweets related to "lung cancer" from last week. The problem is, the first time I've collected ~2000 tweets related to lung cancer on 18th, Sept 2014. But last night, it only gave ~300 tweets, when I looked at the time of the tweets for this new set, it's only collecting tweets from something like ~23:29 to 23:59 on 18th Sept 2014. A large chunk of data is obviously missing. I don't think it's something with my code (below), I have tested various ways including deleting most of the fields to be collected and the time of data is still cut off prematurely.
Is this a known issue with Twitter API (when collecting last 7 days' data)? If so, it will be pretty horrible if someone is trying to do serious research. Or is it somewhere in my code that caused this (note: it runs perfectly fine for other previous/subsequent dates)?
import tweepy
import time
import csv
ckey = ""
csecret = ""
atoken = ""
asecret = ""
OAUTH_KEYS = {'consumer_key':ckey, 'consumer_secret':csecret,
'access_token_key':atoken, 'access_token_secret':asecret}
auth = tweepy.OAuthHandler(OAUTH_KEYS['consumer_key'], OAUTH_KEYS['consumer_secret'])
api = tweepy.API(auth)
# Stream the first "xxx" tweets related to "car", then filter out the ones without geo-enabled
# Reference of search (q) operator: https://dev.twitter.com/rest/public/search
# Common parameters: Changeable only here
startSince = '2014-09-18'
endUntil = '2014-09-20'
suffix = '_18SEP2014.csv'
############################
### Lung cancer starts #####
searchTerms2 = '"lung cancer" OR "lung cancers" OR "lungcancer" OR "lungcancers" OR \
"lung tumor" OR "lungtumor" OR "lung tumors" OR "lungtumors" OR "lung neoplasm"'
# Items from 0 to 500,000 (which *should* cover all tweets)
# Increase by 4,000 for each cycle (because 5000-6000 is over the Twitter rate limit)
# Then wait for 20 min before next request (becaues twitter request wait time is 15min)
counter2 = 0
for tweet in tweepy.Cursor(api.search, q=searchTerms2,
since=startSince, until=endUntil).items(999999999): # changeable here
try:
'''
print "Name:", tweet.author.name.encode('utf8')
print "Screen-name:", tweet.author.screen_name.encode('utf8')
print "Tweet created:", tweet.created_at'''
placeHolder = []
placeHolder.append(tweet.author.name.encode('utf8'))
placeHolder.append(tweet.author.screen_name.encode('utf8'))
placeHolder.append(tweet.created_at)
prefix = 'TweetData_lungCancer'
wholeFileName = prefix + suffix
with open(wholeFileName, "ab") as f: # changeable here
writeFile = csv.writer(f)
writeFile.writerow(placeHolder)
counter2 += 1
if counter2 == 4000:
time.sleep(60*20) # wait for 20 min everytime 4,000 tweets are extracted
counter2 = 0
continue
except tweepy.TweepError:
time.sleep(60*20)
continue
except IOError:
time.sleep(60*2.5)
continue
except StopIteration:
break
Update:
I have since tried running the same python scripts on a different computer (which is faster and more powerful than my home laptop). And the latter resulted in the expected number of tweets, I don't know why it's happening as my home laptop works fine for many programs, but I think we could rest the case and rule out the potential issues related to the scripts or Twitter API.
If you want to collect more data, I would highly recommend the streaming api that Tweepy has to offer. It has a much higher rate limit, in fact I was able to collect 500,000 tweets in just one day.
Also your rate limit checking is not very robust, you don't know for sure that Twitter will allow you to access 4000 tweets. From experience, I found that the more often you hit the rate limit the fewer tweets you are allowed and the longer you have to wait.
I would recommend using:
api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)
so that your application will not exceed the rate limit, alternatively you should check what you have used with:
print (api.rate_limit_status())
and then you can just sleep the thread like you have done.
Also your end date is incorrect. The end date should be '2014-09-21', one higher than whatever todays date is.
I am modelling data for a logit model with 34 dependent variables,and it keep throwing in the singular matrix error , as below -:
Traceback (most recent call last):
File "<pyshell#1116>", line 1, in <module>
test_scores = smf.Logit(m['event'], train_cols,missing='drop').fit()
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/discrete/discrete_model.py", line 1186, in fit
disp=disp, callback=callback, **kwargs)
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/discrete/discrete_model.py", line 164, in fit
disp=disp, callback=callback, **kwargs)
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/base/model.py", line 357, in fit
hess=hess)
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/base/model.py", line 405, in _fit_mle_newton
newparams = oldparams - np.dot(np.linalg.inv(H),
File "/usr/local/lib/python2.7/site-packages/numpy/linalg/linalg.py", line 445, in inv
return wrap(solve(a, identity(a.shape[0], dtype=a.dtype)))
File "/usr/local/lib/python2.7/site-packages/numpy/linalg/linalg.py", line 328, in solve
raise LinAlgError, 'Singular matrix'
LinAlgError: Singular matrix
Which was when I stumpled on this method to reduce the matrix to its independent columns
def independent_columns(A, tol = 0):#1e-05):
"""
Return an array composed of independent columns of A.
Note the answer may not be unique; this function returns one of many
possible answers.
https://stackoverflow.com/q/13312498/190597 (user1812712)
http://math.stackexchange.com/a/199132/1140 (Gerry Myerson)
http://mail.scipy.org/pipermail/numpy-discussion/2008-November/038705.html
(Anne Archibald)
>>> A = np.array([(2,4,1,3),(-1,-2,1,0),(0,0,2,2),(3,6,2,5)])
2 4 1 3
-1 -2 1 0
0 0 2 2
3 6 2 5
# try with checking the rank of matrixs
>>> independent_columns(A)
np.array([[1, 4],
[2, 5],
[3, 6]])
"""
Q, R = linalg.qr(A)
independent = np.where(np.abs(R.diagonal()) > tol)[0]
#print independent
return A[:, independent], independent
A,independent_col_indexes=independent_columns(train_cols.as_matrix(columns=None))
#train_cols will not be converted back from a df to a matrix object,so doing this explicitly
A2=pd.DataFrame(A, columns=train_cols.columns[independent_col_indexes])
test_scores = smf.Logit(m['event'],A2,missing='drop').fit()
I still get the LinAlgError , though I was hoping I will have the reduced matrix rank now.
Also, I see np.linalg.matrix_rank(train_cols) returns 33 (ie. before calling on the independent_columns function total "x" columns was 34(ie, len(train_cols.ix[0])=34 ), meaning I don't have a full rank matrix), while np.linalg.matrix_rank(A2) returns 33 (meaning I have dropped a columns, and yet I still see the LinAlgError , when I run test_scores = smf.Logit(m['event'],A2,missing='drop').fit() , what am I missing ?
reference to the code above -
How to find degenerate rows/columns in a covariance matrix
I tried to start building the model forward,by introducing each variable at a time, which doesn't give me the singular matrix error, but I would rather have a method that is deterministic, and lets me know, what am I doing wrong & how to eliminate these columns.
Edit (updated post the suggestions by #
user333700 below)
1. You are right, "A2" doesn't have the reduced rank of 33 . ie. len(A2.ix[0]) =34 -> meaning the possibly collinear columns are not dropped - should I increase the "tol", tolerance to get rank of A2 (and the numbers of columns thereof) , as 33. If I change the tol to "1e-05" above, then I do get len(A2.ix[0]) =33, which suggests to me that tol >0 (strictly) is one indicator.
After this I just did the same, test_scores = smf.Logit(m['event'],A2,missing='drop').fit(), without nm to get the convergence.
2. Errors post trying 'nm' method. Strange thing though is that if I take just 20,000 rows, I do get the results. Since it is not showing up Memory error, but "Inverting hessian failed, no bse or cov_params available" - I am assuming, there are multiple nearly-similar records - what would you say ?
m = smf.Logit(data['event_custom'].ix[0:1000000] , train_cols.ix[0:1000000],missing='drop')
test_scores=m.fit(start_params=None,method='nm',maxiter=200,full_output=1)
Warning: Maximum number of iterations has been exceeded
Warning (from warnings module):
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/base/model.py", line 374
warn(warndoc, Warning)
Warning: Inverting hessian failed, no bse or cov_params available
test_scores.summary()
Traceback (most recent call last):
File "<pyshell#17>", line 1, in <module>
test_scores.summary()
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/discrete/discrete_model.py", line 2396, in summary
yname_list)
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/discrete/discrete_model.py", line 2253, in summary
use_t=False)
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/iolib/summary.py", line 826, in add_table_params
use_t=use_t)
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/iolib/summary.py", line 447, in summary_params
std_err = results.bse
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/tools/decorators.py", line 95, in __get__
_cachedval = self.fget(obj)
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/base/model.py", line 1037, in bse
return np.sqrt(np.diag(self.cov_params()))
File "/usr/local/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/base/model.py", line 1102, in cov_params
raise ValueError('need covariance of parameters for computing '
ValueError: need covariance of parameters for computing (unnormalized) covariances
Edit 2: (updated post the suggestions by #user333700 below)
Reiterating what I am trying to model - less than about 1% of total
users "convert" (success outcomes) - so I took a balanced sample of
35(+ve) /65 (-ve)
I suspect the model is not robust, though it converges. So, will use "start_params" as the params from earlier iteration, from a different dataset.
This edit is about confirming is the "start_params" can feed into the results as below -:
A,independent_col_indexes=independent_columns(train_cols.as_matrix(columns=None))
A2=pd.DataFrame(A, columns=train_cols.columns[independent_col_indexes])
m = smf.Logit(data['event_custom'], A2,missing='drop')
#m = smf.Logit(data['event_custom'], train_cols,missing='drop')#,method='nm').fit()#This doesnt work, so tried 'nm' which work, but used lasso, as nm did not converge.
test_scores=m.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, \
trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03)
a_good_looking_previous_result.params=test_scores.params #storing the parameters of pass1 to feed into pass2
test_scores.params
bidfloor_Quartile_modified_binned_0 0.305765
connectiontype_binned_0 -0.436798
day_custom_binned_Fri -0.040269
day_custom_binned_Mon 0.138599
day_custom_binned_Sat -0.319997
day_custom_binned_Sun -0.236507
day_custom_binned_Thu -0.058922
user_agent_device_family_binned_iPad -10.793270
user_agent_device_family_binned_iPhone -8.483099
user_agent_masterclass_binned_apple 9.038889
user_agent_masterclass_binned_generic -0.760297
user_agent_masterclass_binned_samsung -0.063522
log_height_width 0.593199
log_height_width_ScreenResolution -0.520836
productivity -1.495373
games 0.706340
entertainment -1.806886
IAB24 2.531467
IAB17 0.650327
IAB14 0.414031
utilities 9.968253
IAB1 1.850786
social_networking -2.814148
IAB3 -9.230780
music 0.019584
IAB9 -0.415559
C(time_day_modified)[(6, 12]]:C(country)[AUS] -0.103003
C(time_day_modified)[(0, 6]]:C(country)[HKG] 0.769272
C(time_day_modified)[(6, 12]]:C(country)[HKG] 0.406882
C(time_day_modified)[(0, 6]]:C(country)[IDN] 0.073306
C(time_day_modified)[(6, 12]]:C(country)[IDN] -0.207568
C(time_day_modified)[(0, 6]]:C(country)[IND] 0.033370
... more params here
Now on a different dataset(pass2, for indexing), I model the same as below -:
ie. I read a new dataframe, do all the variable transformation and then model via Logit as earlier .
m_pass2 = smf.Logit(data['event_custom'], A2_pass2,missing='drop')
test_scores_pass2=m_pass2.fit_regularized(start_params=a_good_looking_previous_result.params, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, \
trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03)
and, possibly keep iterating by picking up "start_params" from earlier passes.
Several points to this:
You need tol > 0 to detect near perfect collinearity, which might also cause numerical problems in later calculations.
Check the number of columns of A2 to see whether a column has really be dropped.
Logit needs to do some non-linear calculations with the exog, so even if the design matrix is not very close to perfect collinearity, the transformed variables for the log-likelihood, derivative or Hessian calculations might still end up being with numerical problems, like singular Hessian.
(All these are floating point problems when we work near floating point precision, 1e-15, 1e-16. There are sometimes differences in the default thresholds for matrix_rank and similar linalg functions which can imply that in some edge cases one function identifies it as singular and another one doesn't.)
The default optimization method for the discrete models including Logit is a simple Newton method, which is fast in reasonably nice cases, but can fail in cases that are badly conditioned. You could try one of the other optimizers which will be one of those in scipy.optimize, method='nm' is usually very robust but slow, method='bfgs' works well in many cases but also can run into convergence problems.
Nevertheless, even when one of the other optimization methods succeeds, it is still necessary to inspect the results. More often than not, a failure with one method means that the model or estimation problem might not be well defined.
A good way to check whether it is just a problem with bad starting values or a specification problem is to run method='nm' first and then run one of the more accurate methods like newton or bfgs using the nm estimate as starting value, and see whether it succeeds from good starting values.
I have written a python 2.7 script to retrieve all my historical data from Xively.
Originally I wrote it in C#, and it works perfectly.
I am limiting the request to 6 hour blocks, to retrieve all stored data.
My version in Python is as follows:
requestString = 'http://api.xively.com/v2/feeds/41189/datastreams/0001.csv?key=YcfzZVxtXxxxxxxxxxxORnVu_dMQ&start=' + requestDate + '&duration=6hours&interval=0&per_page=1000' response = urllib2.urlopen(requestString).read()
The request date is in the correct format, I compared the full c# requestString version and the python one.
Using the above request, I only get 101 lines of data, which equates to a few minutes of results.
My suspicion is that it is the .read() function, it returns about 34k of characters which is far less than the c# version. I tried adding 100000 as an argument to the ad function, but no change in result.
Left another solution wrote in Python 2.7 too.
In my case, got data each 30 minutes because many sensors sent values every minute and Xively API has limited half hour of data to this sent frequency.
It's general module:
for day in datespan(start_datetime, end_datetime, deltatime): # loop increasing deltatime to star_datetime until finish
while(True): # assurance correct retrieval data
try:
response = urllib2.urlopen('https://api.xively.com/v2/feeds/'+str(feed)+'.csv?key='+apikey_xively+'&start='+ day.strftime("%Y-%m-%dT%H:%M:%SZ")+'&interval='+str(interval)+'&duration='+duration) # get data
break
except:
time.sleep(0.3)
raise # try again
cr = csv.reader(response) # return data in columns
print '.'
for row in cr:
if row[0] in id: # choose desired data
f.write(row[0]+","+row[1]+","+row[2]+"\n") # write "id,timestamp,value"
The full script you can find it here: https://github.com/CarlosRufo/scripts/blob/master/python/retrievalDataXively.py
Hope you might help, delighted to answer any questions :)