plt.show() if __name__ == '__main__': model_path = sys.argv[1] df_path = sys.argv[2] y_path = sys.argv[3] # get model model = pickle.load( open(model_path, 'rb') ) # get_features df = pd.read_pickle(df_path) y = pd.read_pickle(y_path) features = np.array(df.columns.tolist()) # Make same splits as training X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.1, random_state=RANDOM_NUM) #make_feat_plot(model,features) #make_part_plot(model, features) #make_pred_plot(model, X_test, y_test) predicted_value_plot(model=model, df=df, column='number_topics', classification=False, discrete_col=False, freq=False, response_label='Subsrciber_Count', xlim=(3,23))
plt.subplots_adjust(top=0.9) # tight_layout causes overlap with suptitle plt.show() return fig, axs if __name__ == '__main__': model_path = sys.argv[1] df_path = sys.argv[2] # get model model = pickle.load( open(model_path, 'rb') ) # get_features df = pd.read_pickle(df_path) features = np.array(df.columns.tolist()) #make_feat_plot(model,features) #fig, axs = make_part_plot(model, features) predicted_value_plot(model=model, df=df, column='day_of_week', classification=True, class_pred='great traction', discrete_col=False, freq=False, response_label='Likelehood')