[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:import plotly.express as px\nimport pandas as pd\nimport numpy as np\n\ndatafile = '../input/covid19-global-forecasting-week-2/train.csv'\ndata = pd.read_csv(datafile)\n\n# %%\nall_region_data = data[pd.isna(data['Province_State'])]\nregion = ['Japan', 'Israel']\n# region = pd.unique(all_region_data['Country_Region']).tolist()\nf_region = []\ntime_list = []\nregion_name = []\nfor ci in range(len(region)):\n    region_data = data[data['Country_Region'] == region[ci]]\n    region_data = region_data[region_data.ConfirmedCases > 0]\n    inc_percentage = (region_data.ConfirmedCases[1:].to_numpy(\n    )-region_data.ConfirmedCases[:-1].to_numpy())/region_data.ConfirmedCases[:-1].to_numpy()\n    # Only considering the countries with effective data\n    if len(np.where(inc_percentage > 0)[0]) > 0:\n        inc_percentage = inc_percentage[np.where(inc_percentage > 0)[0][0]:]\n        f_region.extend(inc_percentage)\n        time_list.extend([i for i in range(len(inc_percentage))])\n        region_name.extend([region[ci] for i in range(len(inc_percentage))])\n    else:\n        pass\nf_df = pd.DataFrame(\n    {'increase': f_region, 'Day': time_list, 'region': region_name})\n\n\n# %%\nsim_data = []\nspeed = [0.01,0.1,1]\nfor batch in range(1,4):\n    result = f'../input/simulation-scripts/outfile_s{batch}.npz'\n    container = np.load(result)\n    speed_batch = f'Sim: speed {speed[batch-1]}'\n\n    sim_result = [container[key] for key in container]\n    num_infected = []\n    for t in range(len(sim_result)):\n        num_infected.append(len(np.where(sim_result[t] < 30)[0]))\n\n    inc_infected = [(num_infected[i+1]-num_infected[i])/num_infected[i]\n                    for i in range(len(num_infected)-1)]\n    infected_growth_df = pd.DataFrame({'increase': inc_infected, 'Day': [\n        i for i in range(len(sim_result)-1)], 'region': speed_batch})\n    sim_data.append(infected_growth_df)\nsim_df = pd.concat(sim_data)\n# %%\nnewf = f_df.append(sim_df)\n\n# %%\nfig = px.line(newf, x='Day',\n              y='increase', color='region')\nfig.update_layout(title='ip patterns of Japan and Israel against 3 simulations',\n                  xaxis_title='Day',\n                  yaxis_title='Increasing percentage',\n                  xaxis=dict(\n                        showline=True,\n                        showgrid=False,\n                        showticklabels=True,\n                        linecolor='rgb(204, 204, 204)',\n                        linewidth=2,\n                        ticks='outside',\n                        tickfont=dict(\n                            family='Arial',\n                            size=12,\n                            color='rgb(82, 82, 82)',\n                        ),\n                  ),\n                  yaxis=dict(\n                      showline=True,\n                      showgrid=False,\n                      showticklabels=True,\n                      linecolor='rgb(204, 204, 204)',\n                      linewidth=2,\n                      ticks='outside',\n                      tickfont=dict(\n                          family='Arial',\n                          size=12,\n                          color='rgb(82, 82, 82)',\n                      ),\n                  ),\n                  autosize=True,\n\n                  plot_bgcolor='white',\n                  height=400, width=600,\n                  )\n\nfig.show()"}, {'role': 'assistant', 'content': "['helper_functions', 'comment_only']"}]