I'm trying this code for featuretools:
features, feature_names = ft.dfs(entityset = es, target_entity = 'demo',
agg_primitives = ['count', 'max', 'time_since_first', 'median', 'time_since_last', 'avg_time_between',
'sum', 'mean'],
trans_primitives = ['is_weekend', 'year', 'week', 'divide_by_feature', 'percentile'])
But I had this error
TypeError Traceback (most recent call last)
<ipython-input-17-89e925ff895d> in <module>
3 agg_primitives = ['count', 'max', 'time_since_first', 'median', 'time_since_last', 'avg_time_between',
4 'sum', 'mean'],
----> 5 trans_primitives = ['is_weekend', 'year', 'week', 'divide_by_feature', 'percentile'])
~/.local/lib/python3.6/site-packages/featuretools/utils/entry_point.py in function_wrapper(*args, **kwargs)
44 ep.on_error(error=e,
45 runtime=runtime)
---> 46 raise e
47
48 # send return value
~/.local/lib/python3.6/site-packages/featuretools/utils/entry_point.py in function_wrapper(*args, **kwargs)
36 # call function
37 start = time.time()
---> 38 return_value = func(*args, **kwargs)
39 runtime = time.time() - start
40 except Exception as e:
~/.local/lib/python3.6/site-packages/featuretools/synthesis/dfs.py in dfs(entities, relationships, entityset, target_entity, cutoff_time, instance_ids, agg_primitives, trans_primitives, groupby_trans_primitives, allowed_paths, max_depth, ignore_entities, ignore_variables, seed_features, drop_contains, drop_exact, where_primitives, max_features, cutoff_time_in_index, save_progress, features_only, training_window, approximate, chunk_size, n_jobs, dask_kwargs, verbose, return_variable_types)
226 n_jobs=n_jobs,
227 dask_kwargs=dask_kwargs,
--> 228 verbose=verbose)
229 return feature_matrix, features
~/.local/lib/python3.6/site-packages/featuretools/computational_backends/calculate_feature_matrix.py in calculate_feature_matrix(features, entityset, cutoff_time, instance_ids, entities, relationships, cutoff_time_in_index, training_window, approximate, save_progress, verbose, chunk_size, n_jobs, dask_kwargs)
265 cutoff_df_time_var=cutoff_df_time_var,
266 target_time=target_time,
--> 267 pass_columns=pass_columns)
268
269 feature_matrix = pd.concat(feature_matrix)
~/.local/lib/python3.6/site-packages/featuretools/computational_backends/calculate_feature_matrix.py in linear_calculate_chunks(chunks, feature_set, approximate, training_window, verbose, save_progress, entityset, no_unapproximated_aggs, cutoff_df_time_var, target_time, pass_columns)
496 no_unapproximated_aggs,
497 cutoff_df_time_var,
--> 498 target_time, pass_columns)
499 feature_matrix.append(_feature_matrix)
500 # Do a manual garbage collection in case objects from calculate_chunk
~/.local/lib/python3.6/site-packages/featuretools/computational_backends/calculate_feature_matrix.py in calculate_chunk(chunk, feature_set, entityset, approximate, training_window, verbose, save_progress, no_unapproximated_aggs, cutoff_df_time_var, target_time, pass_columns)
341 ids,
342 precalculated_features=precalculated_features_trie,
--> 343 training_window=window)
344
345 id_name = _feature_matrix.index.name
~/.local/lib/python3.6/site-packages/featuretools/computational_backends/utils.py in wrapped(*args, **kwargs)
35 def wrapped(*args, **kwargs):
36 if save_progress is None:
---> 37 r = method(*args, **kwargs)
38 else:
39 time = args[0].to_pydatetime()
~/.local/lib/python3.6/site-packages/featuretools/computational_backends/calculate_feature_matrix.py in calc_results(time_last, ids, precalculated_features, training_window)
316 ignored=all_approx_feature_set)
317
--> 318 matrix = calculator.run(ids)
319 return matrix
320
~/.local/lib/python3.6/site-packages/featuretools/computational_backends/feature_set_calculator.py in run(self, instance_ids)
100 precalculated_trie=self.precalculated_features,
101 filter_variable=target_entity.index,
--> 102 filter_values=instance_ids)
103
104 # The dataframe for the target entity should be stored at the root of
~/.local/lib/python3.6/site-packages/featuretools/computational_backends/feature_set_calculator.py in _calculate_features_for_entity(self, entity_id, feature_trie, df_trie, full_entity_df_trie, precalculated_trie, filter_variable, filter_values, parent_data)
187 columns=columns,
188 time_last=self.time_last,
--> 189 training_window=self.training_window)
190
191 # Step 2: Add variables to the dataframe linking it to all ancestors.
~/.local/lib/python3.6/site-packages/featuretools/entityset/entity.py in query_by_values(self, instance_vals, variable_id, columns, time_last, training_window)
271
272 if columns is not None:
--> 273 df = df[columns]
274
275 return df
~/.local/lib/python3.6/site-packages/pandas/core/frame.py in __getitem__(self, key)
2686 return self._getitem_multilevel(key)
2687 else:
-> 2688 return self._getitem_column(key)
2689
2690 def _getitem_column(self, key):
~/.local/lib/python3.6/site-packages/pandas/core/frame.py in _getitem_column(self, key)
2693 # get column
2694 if self.columns.is_unique:
-> 2695 return self._get_item_cache(key)
2696
2697 # duplicate columns & possible reduce dimensionality
~/.local/lib/python3.6/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
2485 """Return the cached item, item represents a label indexer."""
2486 cache = self._item_cache
-> 2487 res = cache.get(item)
2488 if res is None:
2489 values = self._data.get(item)
TypeError: unhashable type: 'set'
I also tried the simplest code for deep feature synthesis (dfs) as shown below, but it still encountered the same error
features, feature_names = ft.dfs(entityset = es, target_entity = 'demo')
I'm not really sure why I encountered this error, any help or recommendations on how to go about from here is deeply appreciated.
Thanks in advance for your help!
I found a solution, my current version had bugs in it that was fixed by the FeatureTools team. Just run pip install directly from master,
pip install --upgrade https://github.com/featuretools/featuretools/zipball/master
This fixed and has been released in Featuretools 0.9.1. If you upgrade to the latest version of Featuretools, it will go away.
Related
I have two pandas dataframes. the dataframes are named stationary_train and test_exog. I have the shape of the dataframes below. I also have some sample data from the dataframes. I'm trying to fit an arima model to the data using ARIMA from statsmodel. I want to forecast the endogeneous variable "stationary_train" using exogeneous variable "test_exog". I'm using the code below. I'm getting the error below, I'm unclear why. the two dataframes are the same shape, and I don't see any missing values in either dataframe. can anyone see what the issue is and suggest how to fix it?
data:
test_exog[:-1].shape
(203, 1)
stationary_train.shape
(203, 1)
print(exog_auto_model.order)
(11, 0, 6)
print(test_exog[:-1].head())
exog_passengers
month
2000-01-01 46513.9
2000-02-01 48555.7
2000-03-01 58812.4
2000-04-01 56101.1
2000-05-01 58237.4
print(stationary_train.head())
passengers
month
2000-02-01 2034.0
2000-03-01 10238.0
2000-04-01 -2731.0
2000-05-01 2168.0
2000-06-01 2872.0
code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# statmodels
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.arima_model import ARMA, ARIMA
# datetime
from datetime import datetime
ARIMA(endog=stationary_train.values.reshape(-1,1),
exog=test_exog[:-1],
order=exog_auto_model.order).fit()
error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-90-ae6d07b32cf7> in <module>
1 exog_predict= ARIMA(endog=stationary_train.values.reshape(-1,1),
2 exog=test_exog[:-1],
----> 3 order=exog_auto_model.order).fit()
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/tsa/arima_model.py in fit(self, start_params, trend, method, transparams, solver, maxiter, full_output, disp, callback, start_ar_lags, **kwargs)
1028 maxiter=maxiter,
1029 full_output=full_output, disp=disp,
-> 1030 callback=callback, **kwargs)
1031 params = mlefit.params
1032
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/base/model.py in fit(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)
525 callback=callback,
526 retall=retall,
--> 527 full_output=full_output)
528
529 # NOTE: this is for fit_regularized and should be generalized
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/base/optimizer.py in _fit(self, objective, gradient, start_params, fargs, kwargs, hessian, method, maxiter, full_output, disp, callback, retall)
225 disp=disp, maxiter=maxiter, callback=callback,
226 retall=retall, full_output=full_output,
--> 227 hess=hessian)
228
229 optim_settings = {'optimizer': method, 'start_params': start_params,
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/base/optimizer.py in _fit_lbfgs(f, score, start_params, fargs, kwargs, disp, maxiter, callback, retall, full_output, hess)
630 callback=callback, args=fargs,
631 bounds=bounds, disp=disp,
--> 632 **extra_kwargs)
633
634 if full_output:
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)
196
197 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
--> 198 **opts)
199 d = {'grad': res['jac'],
200 'task': res['message'],
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)
306 sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
307 bounds=new_bounds,
--> 308 finite_diff_rel_step=finite_diff_rel_step)
309
310 func_and_grad = sf.fun_and_grad
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/optimize.py in _prepare_scalar_function(fun, x0, jac, args, bounds, epsilon, finite_diff_rel_step, hess)
260 # calculation reduces overall function evaluations.
261 sf = ScalarFunction(fun, x0, args, grad, hess,
--> 262 finite_diff_rel_step, bounds, epsilon=epsilon)
263
264 return sf
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in __init__(self, fun, x0, args, grad, hess, finite_diff_rel_step, finite_diff_bounds, epsilon)
74
75 self._update_fun_impl = update_fun
---> 76 self._update_fun()
77
78 # Gradient evaluation
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in _update_fun(self)
164 def _update_fun(self):
165 if not self.f_updated:
--> 166 self._update_fun_impl()
167 self.f_updated = True
168
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in update_fun()
71
72 def update_fun():
---> 73 self.f = fun_wrapped(self.x)
74
75 self._update_fun_impl = update_fun
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in fun_wrapped(x)
68 def fun_wrapped(x):
69 self.nfev += 1
---> 70 return fun(x, *args)
71
72 def update_fun():
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/base/model.py in f(params, *args)
499
500 def f(params, *args):
--> 501 return -self.loglike(params, *args) / nobs
502
503 if method == 'newton':
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/tsa/arima_model.py in loglike(self, params, set_sigma2)
839 method = self.method
840 if method in ['mle', 'css-mle']:
--> 841 return self.loglike_kalman(params, set_sigma2)
842 elif method == 'css':
843 return self.loglike_css(params, set_sigma2)
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/tsa/arima_model.py in loglike_kalman(self, params, set_sigma2)
849 Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.
850 """
--> 851 return KalmanFilter.loglike(params, self, set_sigma2)
852
853 def loglike_css(self, params, set_sigma2=True):
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py in loglike(cls, params, arma_model, set_sigma2)
218 loglike, sigma2 = kalman_loglike.kalman_loglike_double(
219 y, k, k_ar, k_ma, k_lags, int(nobs),
--> 220 Z_mat, R_mat, T_mat)
221 elif np.issubdtype(paramsdtype, np.complex128):
222 loglike, sigma2 = kalman_loglike.kalman_loglike_complex(
statsmodels/tsa/kalmanf/kalman_loglike.pyx in statsmodels.tsa.kalmanf.kalman_loglike.kalman_loglike_double()
statsmodels/tsa/kalmanf/kalman_loglike.pyx in statsmodels.tsa.kalmanf.kalman_loglike.kalman_filter_double()
<__array_function__ internals> in pinv(*args, **kwargs)
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/numpy/linalg/linalg.py in pinv(a, rcond, hermitian)
2001 return wrap(res)
2002 a = a.conjugate()
-> 2003 u, s, vt = svd(a, full_matrices=False, hermitian=hermitian)
2004
2005 # discard small singular values
<__array_function__ internals> in svd(*args, **kwargs)
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/numpy/linalg/linalg.py in svd(a, full_matrices, compute_uv, hermitian)
1659
1660 signature = 'D->DdD' if isComplexType(t) else 'd->ddd'
-> 1661 u, s, vh = gufunc(a, signature=signature, extobj=extobj)
1662 u = u.astype(result_t, copy=False)
1663 s = s.astype(_realType(result_t), copy=False)
ValueError: On entry to DLASCL parameter number 4 had an illegal value
please i really need your help, i'm struggling with MinMaxScaler, i would like to apply this technique on the array below that contains columns with string and numbers. I only want to implement this technique on the columns that contains numbers.
clean_tweets_no_urls = pd.DataFrame(counts_no_urls.most_common(15),
columns=['words', 'count'])
clean_tweets_no_urls.head()
That's my array
minmax_scaling(clean_tweets_no_urls, columns=['words', 'count'])
For that, i'm getting this result :
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-108-eeb7b44d7121> in <module>
----> 1 minmax_scaling(clean_tweets_no_urls, columns=['words', 'count'])
C:\ProgramData\Anaconda3\lib\site-packages\mlxtend\preprocessing\scaling.py in minmax_scaling(array, columns, min_val, max_val)
36
37 """
---> 38 ary_new = array.astype(float)
39 if len(ary_new.shape) == 1:
40 ary_new = ary_new[:, np.newaxis]
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in astype(self, dtype, copy, errors)
5696 else:
5697 # else, only a single dtype is given
-> 5698 new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors)
5699 return self._constructor(new_data).__finalize__(self)
5700
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in astype(self, dtype, copy, errors)
580
581 def astype(self, dtype, copy: bool = False, errors: str = "raise"):
--> 582 return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
583
584 def convert(self, **kwargs):
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in apply(self, f, filter, **kwargs)
440 applied = b.apply(f, **kwargs)
441 else:
--> 442 applied = getattr(b, f)(**kwargs)
443 result_blocks = _extend_blocks(applied, result_blocks)
444
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals\blocks.py in astype(self, dtype, copy, errors)
623 vals1d = values.ravel()
624 try:
--> 625 values = astype_nansafe(vals1d, dtype, copy=True)
626 except (ValueError, TypeError):
627 # e.g. astype_nansafe can fail on object-dtype of strings
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\dtypes\cast.py in astype_nansafe(arr, dtype, copy, skipna)
895 if copy or is_object_dtype(arr) or is_object_dtype(dtype):
896 # Explicit copy, or required since NumPy can't view from / to object.
--> 897 return arr.astype(dtype, copy=True)
898
899 return arr.view(dtype)
ValueError: could not convert string to float: 'joebiden'
from sklearn.preprocessing import minmax_scale
clean_tweets_no_urls[['count']] = minmax_scale.fit_transform(clean_tweets_no_urls[['count']])
This may be used to automate finding numeric columns.
Working through the featuretools "predict_next_purchase" demo against my own data. I've created the entity set, and have also created a new pandas.dataframe comprised of the labels and times. I'm to the point of using ft.dfs for deep feature synthesis, and am getting a RuntimeError: maximum recursion depth exceeded. Below is the stack trace:
feature_matrix, features = ft.dfs(target_entity='projects',
cutoff_time=labels.reset_index().loc[:,['jobnumber','time']],
training_window=inst_defn['training_window'],
entityset=es,
verbose=True)
Stack Trace:
Building features: 0it [00:00, ?it/s]
RuntimeError: maximum recursion depth exceeded
RuntimeErrorTraceback (most recent call last)
<ipython-input-743-f05fc567dd1b> in <module>()
3 training_window=inst_defn['training_window'],
4 entityset=es,
----> 5 verbose=True)
/Users/nbernini/OneDrive/PSC/venv/ml20/lib/python2.7/site-packages/featuretools/synthesis/dfs.pyc in dfs(entities, relationships, entityset, target_entity, cutoff_time, instance_ids, agg_primitives, trans_primitives, allowed_paths, max_depth, ignore_entities, ignore_variables, seed_features, drop_contains, drop_exact, where_primitives, max_features, cutoff_time_in_index, save_progress, features_only, training_window, approximate, verbose)
164 seed_features=seed_features)
165
--> 166 features = dfs_object.build_features(verbose=verbose)
167
168 if features_only:
/Users/nbernini/OneDrive/PSC/venv/ml20/lib/python2.7/site-packages/featuretools/synthesis/deep_feature_synthesis.pyc in build_features(self, variable_types, verbose)
227 self.where_clauses = defaultdict(set)
228 self._run_dfs(self.es[self.target_entity_id], [],
--> 229 all_features, max_depth=self.max_depth)
230
231 new_features = list(all_features[self.target_entity_id].values())
/Users/nbernini/OneDrive/PSC/venv/ml20/lib/python2.7/site-packages/featuretools/synthesis/deep_feature_synthesis.pyc in _run_dfs(self, entity, entity_path, all_features, max_depth)
353 entity_path=list(entity_path),
354 all_features=all_features,
--> 355 max_depth=new_max_depth)
356
357 """
/Users/nbernini/OneDrive/PSC/venv/ml20/lib/python2.7/site-packages/featuretools/synthesis/deep_feature_synthesis.pyc in _run_dfs(self, entity, entity_path, all_features, max_depth)
338 if self._apply_traversal_filters(entity, self.es[b_id],
339 entity_path,
--> 340 forward=False) and
341 b_id not in self.ignore_entities]
342 for b_entity_id in backward_entities:
/Users/nbernini/OneDrive/PSC/venv/ml20/lib/python2.7/site-packages/featuretools/synthesis/deep_feature_synthesis.pyc in _apply_traversal_filters(self, parent_entity, child_entity, entity_path, forward)
429 child_entity=child_entity,
430 target_entity_id=self.target_entity_id,
--> 431 entity_path=entity_path, forward=forward):
432 return False
433
/Users/nbernini/OneDrive/PSC/venv/ml20/lib/python2.7/site-packages/featuretools/synthesis/dfs_filters.pyc in is_valid(self, feature, entity, target_entity_id, child_feature, child_entity, entity_path, forward, where)
53
54 if type(feature) != list:
---> 55 return func(*args)
56
57 else:
/Users/nbernini/OneDrive/PSC/venv/ml20/lib/python2.7/site-packages/featuretools/synthesis/dfs_filters.pyc in apply_filter(self, parent_entity, child_entity, target_entity_id, entity_path, forward)
76 if (parent_entity.id == target_entity_id or
77 es.find_backward_path(parent_entity.id,
---> 78 target_entity_id) is None):
79 return True
80 path = es.find_backward_path(parent_entity.id, child_entity.id)
/Users/nbernini/OneDrive/PSC/venv/ml20/lib/python2.7/site-packages/featuretools/entityset/base_entityset.pyc in find_backward_path(self, start_entity_id, goal_entity_id)
308 is returned if no path exists.
309 """
--> 310 forward_path = self.find_forward_path(goal_entity_id, start_entity_id)
311 if forward_path is not None:
312 return forward_path[::-1]
/Users/nbernini/OneDrive/PSC/venv/ml20/lib/python2.7/site-packages/featuretools/entityset/base_entityset.pyc in find_forward_path(self, start_entity_id, goal_entity_id)
287
288 for r in self.get_forward_relationships(start_entity_id):
--> 289 new_path = self.find_forward_path(r.parent_entity.id, goal_entity_id)
290 if new_path is not None:
291 return [r] + new_path
... last 1 frames repeated, from the frame below ...
/Users/nbernini/OneDrive/PSC/venv/ml20/lib/python2.7/site-packages/featuretools/entityset/base_entityset.pyc in find_forward_path(self, start_entity_id, goal_entity_id)
287
288 for r in self.get_forward_relationships(start_entity_id):
--> 289 new_path = self.find_forward_path(r.parent_entity.id, goal_entity_id)
290 if new_path is not None:
291 return [r] + new_path
RuntimeError: maximum recursion depth exceeded
The issue here is cyclical relationships in your entity set. Currently, Deep Feature Synthesis can only create features when there is one unique path between two entities. If you have an entity with a relationship to itself, you would also get this error.
A future release of Featuretools will offer better support for this use case.
So I have a dataframe X which looks something like this:
X.head()
0 My wife took me here on my birthday for breakf...
1 I have no idea why some people give bad review...
3 Rosie, Dakota, and I LOVE Chaparral Dog Park!!...
4 General Manager Scott Petello is a good egg!!!...
6 Drop what you're doing and drive here. After I...
Name: text, dtype: object
And then,
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer()
X = cv.fit_transform(X)
But I get this error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-61-8ff79b91e317> in <module>()
----> 1 X = cv.fit_transform(X)
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in fit_transform(self, raw_documents, y)
867
868 vocabulary, X = self._count_vocab(raw_documents,
--> 869 self.fixed_vocabulary_)
870
871 if self.binary:
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in _count_vocab(self, raw_documents, fixed_vocab)
790 for doc in raw_documents:
791 feature_counter = {}
--> 792 for feature in analyze(doc):
793 try:
794 feature_idx = vocabulary[feature]
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in <lambda>(doc)
264
265 return lambda doc: self._word_ngrams(
--> 266 tokenize(preprocess(self.decode(doc))), stop_words)
267
268 else:
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in <lambda>(x)
230
231 if self.lowercase:
--> 232 return lambda x: strip_accents(x.lower())
233 else:
234 return strip_accents
~/anaconda3/lib/python3.6/site-packages/scipy/sparse/base.py in __getattr__(self, attr)
574 return self.getnnz()
575 else:
--> 576 raise AttributeError(attr + " not found")
577
578 def transpose(self, axes=None, copy=False):
AttributeError: lower not found
No idea why.
You need to specify the column name of the text data even if the dataframe has single column.
X_countMatrix = cv.fit_transform(X['text'])
Because a CountVectorizer expects an iterable as input and when you supply a dataframe as an argument, only thing thats iterated is the column names. So even if you did not have any errors, that would be incorrect. Lucky that you got an error and got a chance to correct it.
I am using a while loop to calculate a cost function for memory reasons. When calculating the gradient, tensorflow will store Nm tensors where Nm is the number of iterations in my while loop (this cuases the same memory issues I had with the original energy functions). I do not want that as I don't have enough memory. So I want to register a new op along with a gradient function that both use a while loop. However I am having issues with using function.defun and a while loop. To simplify things, I have a small test example below:
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.framework import function
def _run(tensor):
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
res = sess.run(tensor)
return res
#function.Defun(tf.float32,tf.float32,func_name ='tf_test_log')#,grad_func=tf_test_logGrad)
def tf_test_log(t_x,t_y):
#N = t_x.shape[0].value
condition = lambda i,m1: i<N
def body(index,x):
#return[(index+1),tf.concat([x, tf.expand_dims(tf.exp( tf.add( t_x[:,index],t_y[:,index]) ),1) ],1 ) ]
return[(index+1),tf.add(x, tf.exp( tf.add( t_x[:,0],t_y[:,0]) ) ) ]
i0 = tf.constant(0,dtype=tf.int32)
m0 = tf.zeros([N,1],dType)
ijk_0 = [i0,m0]
L,t_log_x = tf.while_loop(condition,body,ijk_0,
shape_invariants=[i0.get_shape(),
tf.TensorShape([N,None])]
)
return t_log_x
dType = tf.float32
N = np.int32(100)
t_N = tf.constant(N,dtype = tf.int32)
t_x = tf.constant(np.random.randn(N,N),dtype = dType)
t_y = tf.constant(np.random.randn(N,N),dtype = dType)
ys = _run(tf_test_log(t_x,t_y))
I then try to test the new op:
I get a Value error: The shape for while/Merge_1:0 is not an invariant for the loop. It enters the loop with shape (100, ?), but has shape after one iteration. Provide shape invariants using either the shape_invariants argument of tf.while_loop or set_shape() on the loop variables.
Note that calling
If i use a concatenate operation (instead of the add operation that gets returned by my while loop), I do not get any issues.
However, If I do not set N as a global variable (i.e. I do N = t_x.shape[0]) inside the body of the tf_test_log function, I get a Value error.
ValueError: Cannot convert a partially known TensorShape to a Tensor: (?, 1)
What is wrong with my code? Any help is greatly appreciated!
I am using python 3.5 on ubuntu 16.04 and tensorflow 1.4
full output:
ValueError Traceback (most recent call last)
~/Documents/TheEffingPhDHatersGonnaHate/PAM/defun_while.py in <module>()
51 t_x = tf.constant(np.random.randn(N,N),dtype = dType)
52 t_y = tf.constant(np.random.randn(N,N),dtype = dType)
---> 53 ys = _run(tf_test_log(t_x,t_y))
54
55
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in __call__(self, *args, **kwargs)
503
504 def __call__(self, *args, **kwargs):
--> 505 self.add_to_graph(ops.get_default_graph())
506 args = [ops.convert_to_tensor(_) for _ in args] + self._extra_inputs
507 ret, op = _call(self._signature, *args, **kwargs)
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in add_to_graph(self, g)
484 def add_to_graph(self, g):
485 """Adds this function into the graph g."""
--> 486 self._create_definition_if_needed()
487
488 # Adds this function into 'g'.
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in _create_definition_if_needed(self)
319 """Creates the function definition if it's not created yet."""
320 with context.graph_mode():
--> 321 self._create_definition_if_needed_impl()
322
323 def _create_definition_if_needed_impl(self):
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in _create_definition_if_needed_impl(self)
336 # Call func and gather the output tensors.
337 with vs.variable_scope("", custom_getter=temp_graph.getvar):
--> 338 outputs = self._func(*inputs)
339
340 # There is no way of distinguishing between a function not returning
~/Documents/TheEffingPhDHatersGonnaHate/PAM/defun_while.py in tf_test_log(t_x, t_y)
39 L,t_log_x = tf.while_loop(condition,body,ijk_0,
40 shape_invariants=[i0.get_shape(),
---> 41 tf.TensorShape([N,None])]
42 )
43 return t_log_x
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in while_loop(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name)
2814 loop_context = WhileContext(parallel_iterations, back_prop, swap_memory) # pylint: disable=redefined-outer-name
2815 ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, loop_context)
-> 2816 result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
2817 return result
2818
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in BuildLoop(self, pred, body, loop_vars, shape_invariants)
2638 self.Enter()
2639 original_body_result, exit_vars = self._BuildLoop(
-> 2640 pred, body, original_loop_vars, loop_vars, shape_invariants)
2641 finally:
2642 self.Exit()
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in _BuildLoop(self, pred, body, original_loop_vars, loop_vars, shape_invariants)
2619 for m_var, n_var in zip(merge_vars, next_vars):
2620 if isinstance(m_var, ops.Tensor):
-> 2621 _EnforceShapeInvariant(m_var, n_var)
2622
2623 # Exit the loop.
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py in _EnforceShapeInvariant(merge_var, next_var)
576 "Provide shape invariants using either the `shape_invariants` "
577 "argument of tf.while_loop or set_shape() on the loop variables."
--> 578 % (merge_var.name, m_shape, n_shape))
579 else:
580 if not isinstance(var, (ops.IndexedSlices, sparse_tensor.SparseTensor)):
ValueError: The shape for while/Merge_1:0 is not an invariant for the loop. It enters the loop with shape (100, ?), but has shape <unknown> after one iteration. Provide shape invariants using either the `shape_invariants` argument of tf.while_loop or set_shape() on the loop variables.
Thanks #Alexandre Passos for the suggestion in the comment above!
The following piece of code is a modification of the original with a set_shape function added inside the body.
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.framework import function
def _run(tensor):
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
res = sess.run(tensor)
return res
#function.Defun(tf.float32,tf.float32,tf.float32,func_name ='tf_test_logGrad')
def tf_test_logGrad(t_x,t_y,grad):
return grad
#function.Defun(tf.float32,tf.float32,func_name ='tf_test_log')#,grad_func=tf_test_logGrad)
def tf_test_log(t_x,t_y):
#N = t_x.shape[0].value
condition = lambda i,m1: i<N
def body(index,x):
#return[(index+1),tf.concat([x, tf.expand_dims(tf.exp( tf.add( t_x[:,index],t_y[:,index]) ),1) ],1 ) ]
x = tf.add(x, tf.exp( tf.add( t_x[:,0],t_y[:,0]) ) )
x.set_shape([N])
return[(index+1), x]
i0 = tf.constant(0,dtype=tf.int32)
m0 = tf.zeros([N],dType)
ijk_0 = [i0,m0]
L,t_log_x = tf.while_loop(condition,body,ijk_0,
shape_invariants=[i0.get_shape(),
tf.TensorShape([N])]
)
return t_log_x
dType = tf.float32
N = np.int32(100)
t_N = tf.constant(N,dtype = tf.int32)
t_x = tf.constant(np.random.randn(N,N),dtype = dType)
t_y = tf.constant(np.random.randn(N,N),dtype = dType)
ys = _run(tf_test_log(t_x,t_y))
The Issue of global N still persists.
You still need to set the shape of the loop tensors as a global variable outside of the defun decorator. If you try to get it from the shape of the inputs of the defun decorator, you get:
TypeError Traceback (most recent call last)
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py in zeros(shape, dtype, name)
1438 shape = tensor_shape.as_shape(shape)
-> 1439 output = constant(zero, shape=shape, dtype=dtype, name=name)
1440 except (TypeError, ValueError):
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name, verify_shape)
207 tensor_util.make_tensor_proto(
--> 208 value, dtype=dtype, shape=shape, verify_shape=verify_shape))
209 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
379 # exception when dtype is set to np.int64
--> 380 if shape is not None and np.prod(shape, dtype=np.int64) == 0:
381 nparray = np.empty(shape, dtype=np_dt)
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/numpy/core/fromnumeric.py in prod(a, axis, dtype, out, keepdims)
2517 return _methods._prod(a, axis=axis, dtype=dtype,
-> 2518 out=out, **kwargs)
2519
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/numpy/core/_methods.py in _prod(a, axis, dtype, out, keepdims)
34 def _prod(a, axis=None, dtype=None, out=None, keepdims=False):
---> 35 return umr_prod(a, axis, dtype, out, keepdims)
36
TypeError: __int__ returned non-int (type NoneType)
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
~/Documents/TheEffingPhDHatersGonnaHate/PAM/defun_while.py in <module>()
52 t_x = tf.constant(np.random.randn(N,N),dtype = dType)
53 t_y = tf.constant(np.random.randn(N,N),dtype = dType)
---> 54 ys = _run(tf_test_log(t_x,t_y))
55
56
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in __call__(self, *args, **kwargs)
503
504 def __call__(self, *args, **kwargs):
--> 505 self.add_to_graph(ops.get_default_graph())
506 args = [ops.convert_to_tensor(_) for _ in args] + self._extra_inputs
507 ret, op = _call(self._signature, *args, **kwargs)
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in add_to_graph(self, g)
484 def add_to_graph(self, g):
485 """Adds this function into the graph g."""
--> 486 self._create_definition_if_needed()
487
488 # Adds this function into 'g'.
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in _create_definition_if_needed(self)
319 """Creates the function definition if it's not created yet."""
320 with context.graph_mode():
--> 321 self._create_definition_if_needed_impl()
322
323 def _create_definition_if_needed_impl(self):
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/function.py in _create_definition_if_needed_impl(self)
336 # Call func and gather the output tensors.
337 with vs.variable_scope("", custom_getter=temp_graph.getvar):
--> 338 outputs = self._func(*inputs)
339
340 # There is no way of distinguishing between a function not returning
~/Documents/TheEffingPhDHatersGonnaHate/PAM/defun_while.py in tf_test_log(t_x, t_y)
33
34 i0 = tf.constant(0,dtype=tf.int32)
---> 35 m0 = tf.zeros([N],dType)
36
37
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py in zeros(shape, dtype, name)
1439 output = constant(zero, shape=shape, dtype=dtype, name=name)
1440 except (TypeError, ValueError):
-> 1441 shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape")
1442 output = fill(shape, constant(zero, dtype=dtype), name=name)
1443 assert output.dtype.base_dtype == dtype
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, preferred_dtype)
834 name=name,
835 preferred_dtype=preferred_dtype,
--> 836 as_ref=False)
837
838
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx)
924
925 if ret is None:
--> 926 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
927
928 if ret is NotImplemented:
~/environments/tf_1_4_gpu/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py in _tensor_shape_tensor_conversion_function(s, dtype, name, as_ref)
248 if not s.is_fully_defined():
249 raise ValueError(
--> 250 "Cannot convert a partially known TensorShape to a Tensor: %s" % s)
251 s_list = s.as_list()
252 int64_value = 0
ValueError: Cannot convert a partially known TensorShape to a Tensor: (?,)