Code 60 error

Led_Zeppelin · Oct-19-2021, 04:37 PM

# check pycaret vrsion
import pycaret
print('PyCaret: %s' % pycaret.__version__)
from sklearn.ensemble import ExtraTreesClassifier
import pandas as pd
from pycaret.classification import setup
from pycaret.classification import compare_models

#define the location o the dataset
url = 'http://raw.githubusercontent.com/jbrownlee/Datasets/master/sonar.csv'
#load the dataset
df = pd.read_csv(url, header=None)
#sumarize the shape of the dataset
print(df.shape)

#set column names as the column number
n_cols = df.shape[1]
df.colums = [str(i) for i in range(n_cols)]

# summarize the first few rows of data
print(df.head(0))

# load the sonar dataset
from pandas import read_csv
#define the location of the dataset
url = 'http://raw.githubusercontent.com/jbrownlee/Datasets/master/sonar.csv'
# load the dataset
df = read_csv(url, header=None)
# summarize the shape of the dataset
print(df.shape)
#  set ccolumnn names as the column number
n_cos = df.shape[1]
df_columns = [str(i) for i in range(n_cols)]
#  summarize the first few rows of data
print(df.head())

grid = setup(data=df, target=df.columns[-1], html=False, silent=True, verbose=False)
# evaluate models and compare  results
best=compare_models()

# tune model hyperparameters
best = tune_model(ExtraTreesClassifier(), n_iter=200)
# report th best model
print(best)

Generates the following error:

Error:# check pycaret vrsion
import pycaret
print('PyCaret: %s' % pycaret.__version__)
from sklearn.ensemble import ExtraTreesClassifier
import pandas as pd
from pycaret.classification import setup
from pycaret.classification import compare_models

#define the location o the dataset
url = 'http://raw.githubusercontent.com/jbrownlee/Datasets/master/sonar.csv'
#load the dataset
df = pd.read_csv(url, header=None)
#sumarize the shape of the dataset
print(df.shape)

#set column names as the column number
n_cols = df.shape[1]
df.colums = [str(i) for i in range(n_cols)]

# summarize the first few rows of data
print(df.head(0))

# load the sonar dataset
from pandas import read_csv
#define the location of the dataset
url = 'http://raw.githubusercontent.com/jbrownlee/Datasets/master/sonar.csv'
# load the dataset
df = read_csv(url, header=None)
# summarize the shape of the dataset
print(df.shape)
#  set ccolumnn names as the column number
n_cos = df.shape[1]
df_columns = [str(i) for i in range(n_cols)]
#  summarize the first few rows of data
print(df.head())

grid = setup(data=df, target=df.columns[-1], html=False, silent=True, verbose=False)
# evaluate models and compare  results
best=compare_models()

# tune model hyperparameters
best = tune_model(ExtraTreesClassifier(), n_iter=200)
# report th best model
print(best)

I am baffled. What is a code 60 error.

Any help appreciated. Thanks in advance.

Respectfully,

LZ

bowlofred · Oct-19-2021, 04:42 PM

Looks like you've reposted the code rather than error/traceback. Can you post the error?

ndc85430 · Oct-19-2021, 04:43 PM

It doesn't look like you've posted any errors; you've just posted the code again.

Led_Zeppelin · Oct-19-2021, 05:46 PM

Error:PyCaret: 2.3.0
(208, 61)
Empty DataFrame
Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60]
Index: []
(208, 61)
       0       1       2       3       4       5       6       7       8   \
0  0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   
1  0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337   
2  0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598   
3  0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598   
4  0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   

       9       10      11      12      13      14      15      16      17  \
0  0.2111  0.1609  0.1582  0.2238  0.0645  0.0660  0.2273  0.3100  0.2999   
1  0.2872  0.4918  0.6552  0.6919  0.7797  0.7464  0.9444  1.0000  0.8874   
2  0.6194  0.6333  0.7060  0.5544  0.5320  0.6479  0.6931  0.6759  0.7551   
3  0.1264  0.0881  0.1992  0.0184  0.2261  0.1729  0.2131  0.0693  0.2281   
4  0.4459  0.4152  0.3952  0.4256  0.4135  0.4528  0.5326  0.7306  0.6193   

       18      19      20      21      22      23      24      25      26  \
0  0.5078  0.4797  0.5783  0.5071  0.4328  0.5550  0.6711  0.6415  0.7104   
1  0.8024  0.7818  0.5212  0.4052  0.3957  0.3914  0.3250  0.3200  0.3271   
2  0.8929  0.8619  0.7974  0.6737  0.4293  0.3648  0.5331  0.2413  0.5070   
3  0.4060  0.3973  0.2741  0.3690  0.5556  0.4846  0.3140  0.5334  0.5256   
4  0.2032  0.4636  0.4148  0.4292  0.5730  0.5399  0.3161  0.2285  0.6995   

       27      28      29      30      31      32      33      34      35  \
0  0.8080  0.6791  0.3857  0.1307  0.2604  0.5121  0.7547  0.8537  0.8507   
1  0.2767  0.4423  0.2028  0.3788  0.2947  0.1984  0.2341  0.1306  0.4182   
2  0.8533  0.6036  0.8514  0.8512  0.5045  0.1862  0.2709  0.4232  0.3043   
3  0.2520  0.2090  0.3559  0.6260  0.7340  0.6120  0.3497  0.3953  0.3012   
4  1.0000  0.7262  0.4724  0.5103  0.5459  0.2881  0.0981  0.1951  0.4181   

       36      37      38      39      40      41      42      43      44  \
0  0.6692  0.6097  0.4943  0.2744  0.0510  0.2834  0.2825  0.4256  0.2641   
1  0.3835  0.1057  0.1840  0.1970  0.1674  0.0583  0.1401  0.1628  0.0621   
2  0.6116  0.6756  0.5375  0.4719  0.4647  0.2587  0.2129  0.2222  0.2111   
3  0.5408  0.8814  0.9857  0.9167  0.6121  0.5006  0.3210  0.3202  0.4295   
4  0.4604  0.3217  0.2828  0.2430  0.1979  0.2444  0.1847  0.0841  0.0692   

       45      46      47      48      49      50      51      52      53  \
0  0.1386  0.1051  0.1343  0.0383  0.0324  0.0232  0.0027  0.0065  0.0159   
1  0.0203  0.0530  0.0742  0.0409  0.0061  0.0125  0.0084  0.0089  0.0048   
2  0.0176  0.1348  0.0744  0.0130  0.0106  0.0033  0.0232  0.0166  0.0095   
3  0.3654  0.2655  0.1576  0.0681  0.0294  0.0241  0.0121  0.0036  0.0150   
4  0.0528  0.0357  0.0085  0.0230  0.0046  0.0156  0.0031  0.0054  0.0105   

       54      55      56      57      58      59 60  
0  0.0072  0.0167  0.0180  0.0084  0.0090  0.0032  R  
1  0.0094  0.0191  0.0140  0.0049  0.0052  0.0044  R  
2  0.0180  0.0244  0.0316  0.0164  0.0095  0.0078  R  
3  0.0085  0.0073  0.0050  0.0044  0.0040  0.0117  R  
4  0.0110  0.0015  0.0072  0.0048  0.0107  0.0094  R  
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~\miniconda3\envs\pycaret_env\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2897             try:
-> 2898                 return self._engine.get_loc(casted_key)
   2899             except KeyError as err:

pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 60

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
<ipython-input-1-b411004c7c69> in <module>
     35 print(df.head())
     36 
---> 37 grid = setup(data=df, target=df.columns[-1], html=False, silent=True, verbose=False)
     38 # evaluate models and compare  results
     39 best=compare_models()

~\miniconda3\envs\pycaret_env\lib\site-packages\pycaret\classification.py in setup(data, target, train_size, test_data, preprocess, imputation_type, iterative_imputation_iters, categorical_features, categorical_imputation, categorical_iterative_imputer, ordinal_features, high_cardinality_features, high_cardinality_method, numeric_features, numeric_imputation, numeric_iterative_imputer, date_features, ignore_features, normalize, normalize_method, transformation, transformation_method, handle_unknown_categorical, unknown_categorical_method, pca, pca_method, pca_components, ignore_low_variance, combine_rare_levels, rare_level_threshold, bin_numeric_features, remove_outliers, outliers_threshold, remove_multicollinearity, multicollinearity_threshold, remove_perfect_collinearity, create_clusters, cluster_iter, polynomial_features, polynomial_degree, trigonometry_features, polynomial_threshold, group_features, group_names, feature_selection, feature_selection_threshold, feature_selection_method, feature_interaction, feature_ratio, interaction_threshold, fix_imbalance, fix_imbalance_method, data_split_shuffle, data_split_stratify, fold_strategy, fold, fold_shuffle, fold_groups, n_jobs, use_gpu, custom_pipeline, html, session_id, log_experiment, experiment_name, log_plots, log_profile, log_data, silent, verbose, profile, profile_kwargs)
    652         verbose=verbose,
    653         profile=profile,
--> 654         profile_kwargs=profile_kwargs,
    655     )
    656 

~\miniconda3\envs\pycaret_env\lib\site-packages\pycaret\internal\tabular.py in setup(data, target, ml_usecase, available_plots, train_size, test_data, preprocess, imputation_type, iterative_imputation_iters, categorical_features, categorical_imputation, categorical_iterative_imputer, ordinal_features, high_cardinality_features, high_cardinality_method, numeric_features, numeric_imputation, numeric_iterative_imputer, date_features, ignore_features, normalize, normalize_method, transformation, transformation_method, handle_unknown_categorical, unknown_categorical_method, pca, pca_method, pca_components, ignore_low_variance, combine_rare_levels, rare_level_threshold, bin_numeric_features, remove_outliers, outliers_threshold, remove_multicollinearity, multicollinearity_threshold, remove_perfect_collinearity, create_clusters, cluster_iter, polynomial_features, polynomial_degree, trigonometry_features, polynomial_threshold, group_features, group_names, feature_selection, feature_selection_threshold, feature_selection_method, feature_interaction, feature_ratio, interaction_threshold, fix_imbalance, fix_imbalance_method, transform_target, transform_target_method, data_split_shuffle, data_split_stratify, fold_strategy, fold, fold_shuffle, fold_groups, n_jobs, use_gpu, custom_pipeline, html, session_id, log_experiment, experiment_name, log_plots, log_profile, log_data, silent, verbose, profile, profile_kwargs, display)
   1173         feature_interactions_top_features_to_select_percentage=interaction_threshold,
   1174         display_types=display_dtypes_pass,  # this is for inferred input box
-> 1175         random_state=seed,
   1176     )
   1177 

~\miniconda3\envs\pycaret_env\lib\site-packages\pycaret\internal\preprocess.py in Preprocess_Path_One(train_data, target_variable, ml_usecase, test_data, categorical_features, numerical_features, time_features, features_todrop, display_types, imputation_type, numeric_imputation_strategy, categorical_imputation_strategy, imputation_classifier, imputation_regressor, imputation_max_iter, imputation_warm_start, imputation_order, apply_zero_nearZero_variance, club_rare_levels, rara_level_threshold_percentage, apply_untrained_levels_treatment, untrained_levels_treatment_method, apply_ordinal_encoding, ordinal_columns_and_categories, apply_cardinality_reduction, cardinal_method, cardinal_features, apply_binning, features_to_binn, apply_grouping, group_name, features_to_group_ListofList, apply_polynomial_trigonometry_features, max_polynomial, trigonometry_calculations, top_poly_trig_features_to_select_percentage, scale_data, scaling_method, Power_transform_data, Power_transform_method, remove_outliers, outlier_contamination_percentage, outlier_methods, dummify_categoricals, apply_feature_selection, feature_selection_top_features_percentage, feature_selection_method, remove_multicollinearity, maximum_correlation_between_features, remove_perfect_collinearity, apply_feature_interactions, feature_interactions_to_apply, feature_interactions_top_features_to_select_percentage, cluster_entire_data, range_of_clusters_to_try, apply_pca, pca_method, pca_variance_retained_or_number_of_components, random_state, n_jobs)
   3309     else:
   3310         # WE NEED TO AUTO INFER the ml use case
-> 3311         inferred_ml_usecase, subcase = infer_ml_usecase(train_data[target_variable])
   3312         if ml_usecase is None:
   3313             ml_usecase = inferred_ml_usecase

~\miniconda3\envs\pycaret_env\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   2904             if self.columns.nlevels > 1:
   2905                 return self._getitem_multilevel(key)
-> 2906             indexer = self.columns.get_loc(key)
   2907             if is_integer(indexer):
   2908                 indexer = [indexer]

~\miniconda3\envs\pycaret_env\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2898                 return self._engine.get_loc(casted_key)
   2899             except KeyError as err:
-> 2900                 raise KeyError(key) from err
   2901 
   2902         if tolerance is not None:

KeyError: 60

Okay, I made this mistake. Here is the error posted above.

Respectfully,

LZ

Code 60 error

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