added exception checking on all modules

pycaret/anomaly.py

@@ -482,6 +482,30 @@ def setup(data,
         if type(group_names) is not list:
             sys.exit('(Type Error): group_names must be of type list. ')        
 
+    #silent
+    if type(silent) is not bool:
+        sys.exit("(Type Error): silent parameter only accepts True or False. ")
+        
+    #html
+    if type(html) is not bool:
+        sys.exit('(Type Error): html parameter only accepts True or False.')
+
+    #logging
+    if type(logging) is not bool:
+        sys.exit('(Type Error): logging parameter only accepts True or False.')
+
+    #log_plots
+    if type(log_plots) is not bool:
+        sys.exit('(Type Error): log_plots parameter only accepts True or False.')
+
+    #log_data
+    if type(log_data) is not bool:
+        sys.exit('(Type Error): log_data parameter only accepts True or False.')
+
+    #log_profile
+    if type(log_profile) is not bool:
+        sys.exit('(Type Error): log_profile parameter only accepts True or False.')
+        
     
     """
     error handling ends here
@@ -3503,4 +3527,48 @@ def get_outliers(data,
     
     dataset = assign_model(c, verbose=False)
     
-    return dataset
\ No newline at end of file
+    return dataset
+
+def models():
+
+    """
+    returns table of models available in model library
+    """
+
+    import pandas as pd
+
+    model_id = ['abod', 'iforest', 'cluster', 'cof', 'histogram', 'knn', 'lof', 'svm', 'pca', 'mcd', 'sod', 'sos']
+
+    model_name = ['Angle-base Outlier Detection',
+                  'Isolation Forest',
+                  'Clustering-Based Local Outlier',
+                  'Connectivity-Based Outlier Factor',
+                  'Histogram-based Outlier Detection',
+                  'k-Nearest Neighbors Detector',
+                  'Local Outlier Factor',
+                  'One-class SVM detector',
+                  'Principal Component Analysis',
+                  'Minimum Covariance Determinant',
+                  'Subspace Outlier Detection',
+                  'Stochastic Outlier Selection']
+
+    model_ref = ['pyod.models.abod.ABOD',
+                  'pyod.models.iforest',
+                  'pyod.models.cblof',
+                  'pyod.models.cof',
+                  'pyod.models.hbos',
+                  'pyod.models.knn',
+                  'pyod.models.lof',
+                  'pyod.models.ocsvm',
+                  'pyod.models.pca',
+                  'pyod.models.mcd',
+                  'pyod.models.sod',
+                  'pyod.models.sos']
+
+    df = pd.DataFrame({'ID' : model_id, 
+                        'Name' : model_name,
+                        'Reference' : model_ref})
+
+    df.set_index('ID', inplace=True)
+
+    return df
\ No newline at end of file

pycaret/classification.py

@@ -721,6 +721,51 @@ def setup(data,
     if type(silent) is not bool:
         sys.exit("(Type Error): silent parameter only accepts True or False. ")
     
+    #remove_perfect_collinearity
+    if type(remove_perfect_collinearity) is not bool:
+        sys.exit('(Type Error): remove_perfect_collinearity parameter only accepts True or False.')
+
+    #html
+    if type(html) is not bool:
+        sys.exit('(Type Error): html parameter only accepts True or False.')
+
+    #folds_shuffle
+    if type(folds_shuffle) is not bool:
+        sys.exit('(Type Error): folds_shuffle parameter only accepts True or False.')
+
+    #data_split_shuffle
+    if type(data_split_shuffle) is not bool:
+        sys.exit('(Type Error): data_split_shuffle parameter only accepts True or False.')
+
+    #logging
+    if type(logging) is not bool:
+        sys.exit('(Type Error): logging parameter only accepts True or False.')
+
+    #log_plots
+    if type(log_plots) is not bool:
+        sys.exit('(Type Error): log_plots parameter only accepts True or False.')
+
+    #log_data
+    if type(log_data) is not bool:
+        sys.exit('(Type Error): log_data parameter only accepts True or False.')
+
+    #log_profile
+    if type(log_profile) is not bool:
+        sys.exit('(Type Error): log_profile parameter only accepts True or False.')
+
+    #fix_imbalance
+    if type(fix_imbalance) is not bool:
+        sys.exit('(Type Error): fix_imbalance parameter only accepts True or False.')
+
+    #fix_imbalance_method
+    if fix_imbalance_method is not None:
+        if hasattr(fix_imbalance_method, 'fit_sample'):
+            pass
+        else:
+            sys.exit('(Type Error): fix_imbalance_method must contain resampler with fit_sample method.')
+
+
+
     #pre-load libraries
     import pandas as pd
     import ipywidgets as ipw
@@ -10806,7 +10851,7 @@ def pull():
 
 def models():
     """
-    returns table of models available in model zoo
+    returns table of models available in model library
     """
 
     import pandas as pd

pycaret/clustering.py

@@ -482,7 +482,30 @@ def setup(data,
         if type(group_names) is not list:
             sys.exit('(Type Error): group_names must be of type list. ')        
 
-    
+    #silent
+    if type(silent) is not bool:
+        sys.exit("(Type Error): silent parameter only accepts True or False. ")
+        
+    #html
+    if type(html) is not bool:
+        sys.exit('(Type Error): html parameter only accepts True or False.')
+
+    #logging
+    if type(logging) is not bool:
+        sys.exit('(Type Error): logging parameter only accepts True or False.')
+
+    #log_plots
+    if type(log_plots) is not bool:
+        sys.exit('(Type Error): log_plots parameter only accepts True or False.')
+
+    #log_data
+    if type(log_data) is not bool:
+        sys.exit('(Type Error): log_data parameter only accepts True or False.')
+
+    #log_profile
+    if type(log_profile) is not bool:
+        sys.exit('(Type Error): log_profile parameter only accepts True or False.')
+
     """
     error handling ends here
     """
@@ -1088,15 +1111,15 @@ def create_model(model = None,
 
     Model                              Abbreviated String   Original Implementation 
     ---------                          ------------------   -----------------------
-    K-Means clustering                 'kmeans'             sklearn.cluster.KMeans.html
+    K-Means Clustering                 'kmeans'             sklearn.cluster.KMeans.html
     Affinity Propagation               'ap'                 AffinityPropagation.html
-    Mean shift clustering              'meanshift'          sklearn.cluster.MeanShift.html
+    Mean shift Clustering              'meanshift'          sklearn.cluster.MeanShift.html
     Spectral Clustering                'sc'                 SpectralClustering.html
     Agglomerative Clustering           'hclust'             AgglomerativeClustering.html
     Density-Based Spatial Clustering   'dbscan'             sklearn.cluster.DBSCAN.html
     OPTICS Clustering                  'optics'             sklearn.cluster.OPTICS.html
     Birch Clustering                   'birch'              sklearn.cluster.Birch.html
-    K-Modes clustering                 'kmodes'             git/nicodv/kmodes
+    K-Modes Clustering                 'kmodes'             git/nicodv/kmodes
     
     num_clusters: int, default = None
     Number of clusters to be generated with the dataset. If None, num_clusters is set to 4. 
@@ -3754,4 +3777,42 @@ def get_clusters(data,
     except:
         c = create_model(model=model, verbose=False, system=False)
     dataset = assign_model(c, verbose=False)
-    return dataset
\ No newline at end of file
+    return dataset
+
+def models():
+
+    """
+    returns table of models available in model library
+    """
+
+    import pandas as pd
+
+    model_id = ['kmeans', 'ap', 'meanshift', 'sc', 'hclust', 'dbscan', 'optics', 'birch', 'kmodes']
+
+    model_name = ['K-Means Clustering',
+                  'Affinity Propagation',
+                  'Mean shift Clustering',
+                  'Spectral Clustering',
+                  'Agglomerative Clustering',
+                  'Density-Based Spatial Clustering',
+                  'OPTICS Clustering',
+                  'Birch Clustering',
+                  'K-Modes Clustering']
+
+    model_ref = ['sklearn.cluster.KMeans',
+                  'sklearn.cluster.AffinityPropagation',
+                  'sklearn.cluster.MeanShift',
+                  'sklearn.cluster.SpectralClustering',
+                  'sklearn.cluster.AgglomerativeClustering',
+                  'sklearn.cluster.DBSCAN',
+                  'sklearn.cluster.OPTICS',
+                  'sklearn.cluster.Birch',
+                  'git/nicodv/kmodes']
+
+    df = pd.DataFrame({'ID' : model_id, 
+                        'Name' : model_name,
+                        'Reference' : model_ref})
+
+    df.set_index('ID', inplace=True)
+
+    return df
\ No newline at end of file

pycaret/nlp.py

@@ -143,7 +143,30 @@ def setup(data,
     except:
         sys.exit('(Type Error): spacy english model is not yet downloaded. See the documentation of setup to see installation guide.')
     
-    
+    #silent
+    if type(silent) is not bool:
+        sys.exit("(Type Error): silent parameter only accepts True or False. ")
+        
+    #html
+    if type(html) is not bool:
+        sys.exit('(Type Error): html parameter only accepts True or False.')
+
+    #logging
+    if type(logging) is not bool:
+        sys.exit('(Type Error): logging parameter only accepts True or False.')
+
+    #log_plots
+    if type(log_plots) is not bool:
+        sys.exit('(Type Error): log_plots parameter only accepts True or False.')
+
+    #log_data
+    if type(log_data) is not bool:
+        sys.exit('(Type Error): log_data parameter only accepts True or False.')
+
+    #verbose
+    if type(verbose) is not bool:
+        sys.exit('(Type Error): verbose parameter only accepts True or False.')
+
     """
     error handling ends here
     """
@@ -3200,3 +3223,32 @@ def get_topics(data, text, model=None, num_topics=4):
     return dataset
 
 
+def models():
+
+    """
+    returns table of models available in model library
+    """
+
+    import pandas as pd
+
+    model_id = ['lda', 'lsi', 'hdp', 'rp', 'nmf']
+
+    model_name = ['Latent Dirichlet Allocation',
+                  'Latent Semantic Indexing',
+                  'Hierarchical Dirichlet Process',
+                  'Random Projections',
+                  'Non-Negative Matrix Factorization']
+
+    model_ref = ['gensim/models/ldamodel',
+                  'gensim/models/lsimodel',
+                  'gensim/models/hdpmodel',
+                  'gensim/models/rpmodel',
+                  'sklearn.decomposition.NMF']
+
+    df = pd.DataFrame({'ID' : model_id, 
+                        'Name' : model_name,
+                        'Reference' : model_ref})
+
+    df.set_index('ID', inplace=True)
+
+    return df
\ No newline at end of file

pycaret/regression.py

@@ -711,7 +711,40 @@ def setup(data,
     #silent
     if type(silent) is not bool:
         sys.exit("(Type Error): silent parameter only accepts True or False. ")
+
+    #remove_perfect_collinearity
+    if type(remove_perfect_collinearity) is not bool:
+        sys.exit('(Type Error): remove_perfect_collinearity parameter only accepts True or False.')
         
+    #html
+    if type(html) is not bool:
+        sys.exit('(Type Error): html parameter only accepts True or False.')
+
+    #folds_shuffle
+    if type(folds_shuffle) is not bool:
+        sys.exit('(Type Error): folds_shuffle parameter only accepts True or False.')
+
+    #data_split_shuffle
+    if type(data_split_shuffle) is not bool:
+        sys.exit('(Type Error): data_split_shuffle parameter only accepts True or False.')
+
+    #logging
+    if type(logging) is not bool:
+        sys.exit('(Type Error): logging parameter only accepts True or False.')
+
+    #log_plots
+    if type(log_plots) is not bool:
+        sys.exit('(Type Error): log_plots parameter only accepts True or False.')
+
+    #log_data
+    if type(log_data) is not bool:
+        sys.exit('(Type Error): log_data parameter only accepts True or False.')
+
+    #log_profile
+    if type(log_profile) is not bool:
+        sys.exit('(Type Error): log_profile parameter only accepts True or False.')
+
+
     #pre-load libraries
     import pandas as pd
     import ipywidgets as ipw
@@ -9343,4 +9376,78 @@ def automl(optimize='r2', use_holdout=False):
 def pull():
     return display_container[-1]
 
+def models():
+    """
+    returns table of models available in model library
+    """
+
+    import pandas as pd
+
+    model_id = ['lr', 'lasso', 'ridge', 'en', 'lar', 'llar', 'omp', 'br', 'ard', 'par', 
+                'ransac', 'tr', 'huber', 'kr', 'svm', 'knn', 'dt', 'rf', 'et', 'ada', 
+                'gbr', 'mlp', 'xgboost', 'lightgbm', 'catboost']
+
+    model_name = ['Linear Regression',
+                   'Lasso Regression',
+                   'Ridge Regression',
+                   'Elastic Net',
+                   'Least Angle Regression',
+                   'Lasso Least Angle Regression',
+                   'Orthogonal Matching Pursuit',
+                   'Bayesian Ridge',
+                   'Automatic Relevance Determination',
+                   'Passive Aggressive Regressor',
+                   'Random Sample Consensus',
+                   'TheilSen Regressor',
+                   'Huber Regressor',
+                   'Kernel Ridge',
+                   'Support Vector Machine',
+                   'K Neighbors Regressor',
+                   'Decision Tree',
+                   'Random Forest',
+                   'Extra Trees Regressor',
+                   'AdaBoost Regressor',
+                   'Gradient Boosting Regressor',
+                   'Multi Level Perceptron',
+                   'Extreme Gradient Boosting',
+                   'Light Gradient Boosting Machine',
+                   'CatBoost Regressor']
 
+    model_ref = ['sklearn.linear_model.LinearRegression',
+                'sklearn.linear_model.Lasso',
+                'sklearn.linear_model.Ridge',
+                'sklearn.linear_model.ElasticNet',
+                'sklearn.linear_model.Lars',
+                'sklearn.linear_model.LassoLars',
+                'sklearn.linear_model.OMP',
+                'sklearn.linear_model.BayesianRidge',
+                'sklearn.linear_model.ARDRegression',
+                'sklearn.linear_model.PAR',
+                'sklearn.linear_model.RANSACRegressor',
+                'sklearn.linear_model.TheilSenRegressor',
+                'sklearn.linear_model.HuberRegressor',
+                'sklearn.kernel_ridge.KernelRidge',
+                'sklearn.svm.SVR',
+                'sklearn.neighbors.KNeighborsRegressor',
+                'sklearn.tree.DecisionTreeRegressor',
+                'sklearn.ensemble.RandomForestRegressor',
+                'sklearn.ensemble.ExtraTreesRegressor',
+                'sklearn.ensemble.AdaBoostRegressor',
+                'sklearn.ensemble.GradientBoostingRegressor',
+                'sklearn.neural_network.MLPRegressor',
+                'xgboost.readthedocs.io',
+                'github.com/microsoft/LightGBM',
+                'https://catboost.ai']
+    
+    model_turbo = [True, True, True, True, True, True, True, True, False, True,
+                   True, True, True, False, True, True, True, True, True, True,
+                   True, False, True, True, True]
+
+    df = pd.DataFrame({'ID' : model_id, 
+                       'Name' : model_name,
+                       'Reference' : model_ref,
+                        'Turbo' : model_turbo})
+
+    df.set_index('ID', inplace=True)
+
+    return df
\ No newline at end of file

pycaret/utils.py

@@ -3,7 +3,7 @@
 # License: MIT
 
 def version():
-    print("pycaret-nightly-0.14")
+    print("pycaret-nightly-0.15")
 
 
 def check_metric(actual, prediction, metric, round=4):

setup.py

@@ -13,7 +13,7 @@ with open('requirements.txt') as f:
 
 setup(
     name="pycaret-nightly",
-    version="0.14",
+    version="0.15",
     description="Nightly build of PyCaret - An open source, low-code machine learning library in Python.",
     long_description=readme(),
     long_description_content_type="text/markdown",