def analyzeVisualize(sentiment):
with open('pickled/pipeline_holdout.pickle', 'rb') as pipeline_holdout:
pipeline = pickle.load(pipeline_holdout)
with open('pickled/metrics_cm_holdout.pickle', 'rb') as metrics_cm:
clf_cm = pickle.load(metrics_cm)
with open('pickled/metrics_cr_holdout.pickle', 'rb') as metrics_cr:
clf_cr = pickle.load(metrics_cr)
with open('pickled/metrics_dataframe.pickle', 'rb') as df:
metrics_df = pickle.load(df)
with open('pickled/metrics_dataframe_kfold.pickle', 'rb') as df:
metrics_df_kfold = pickle.load(df)
clf_svc = pipeline[2]
clf_names = sentiment.clf_names
labels = ['negative', 'positive']
visualizer = DataVisualizer()
for cm, cr, name in zip(clf_cm, clf_cr, clf_names):
visualizer.plotConfusionMatrix(cm, labels, name)
visualizer.plotClassificationReport(cr, labels, name)
metrics_df.rename(columns = {"Accuracy": "value_Accuracy", "Precision": "value_Precision", "Recall": "value_Recall", "F1-score": "value_F1-score", "ROC AUC": "value_ROC AUC"}, inplace = True)
metrics_df['id'] = metrics_df.index
metrics_df_long = pd.wide_to_long(metrics_df, stubnames = 'value', i = 'id', j = 'id_m', sep = '_', suffix = '\w')
metrics_df_long['Metrics'] = metrics_df_long.index.get_level_values('id_m')
visualizer.plotClassifierPerformanceComparison(metrics_df_long, clf_names, 'Holdout')
metrics_df_kfold.rename(columns = {"Accuracy": "value_Accuracy", "Precision": "value_Precision", "Recall": "value_Recall", "F1-score": "value_F1-score", "ROC AUC": "value_ROC AUC"}, inplace = True)
metrics_df_kfold['id'] = metrics_df_kfold.index
metrics_df_kfold_long = pd.wide_to_long(metrics_df_kfold, stubnames = 'value', i = 'id', j = 'id_m', sep = '_', suffix = '\w')
metrics_df_kfold_long['Metrics'] = metrics_df_kfold_long.index.get_level_values('id_m')
visualizer.plotClassifierPerformanceComparison(metrics_df_kfold_long, clf_names, 'K-Fold')
util = Utility()
data = util.classifiersVsFeatures()
colors = ['blue', 'yellow', 'red', 'green']
visualizer.plotClassifiersVsFeatures(data, clf_names, colors)
top_features = util.showTopFeatures(clf_svc, n = 30)
print('The 30 most informative features for both positive and negative coefficients:\n')
print(top_features)
