def plot_all_matched_vals(df): param_keys = set(df.keys()) - set(['test', 'time', 'train', 'test_sample', 'train_sample']) for key in param_keys: if len(df[key].unique()) > 1: dfs, unique_vals = get_dfs_for_matched_exps_with_different_vals( df, key) if len(dfs[0]) > 0: plt.figure() plot_per_sub_unique_vals(df, key, matched=True) plt.title(key, fontsize=12)
def plot_all_matched_vals(df): param_keys = set(df.keys()) - set( ['test', 'time', 'train', 'test_sample', 'train_sample']) for key in param_keys: if len(df[key].unique()) > 1: dfs, unique_vals = get_dfs_for_matched_exps_with_different_vals( df, key) if len(dfs[0]) > 0: plt.figure() plot_per_sub_unique_vals(df, key, matched=True) plt.title(key, fontsize=12)
def plot_per_sub_unique_vals(df, col_name, values_fn=lambda df: df.test, matched=False): assert len(df) > 0 if matched: dfs, unique_vals = get_dfs_for_matched_exps_with_different_vals(df, col_name) else: unique_vals = df[col_name].unique() dfs = [df[df[col_name] == val] for val in unique_vals] plot_per_sub_dfs(dfs, values_fn=values_fn) plt.xticks(range(len(dfs) * 3), np.tile(unique_vals,3), rotation=30, ha='right')
def plot_per_sub_unique_vals(df, col_name, values_fn=lambda df: df.test, matched=False): assert len(df) > 0 if matched: dfs, unique_vals = get_dfs_for_matched_exps_with_different_vals( df, col_name) else: unique_vals = df[col_name].unique() dfs = [df[df[col_name] == val] for val in unique_vals] plot_per_sub_dfs(dfs, values_fn=values_fn) plt.xticks(range(len(dfs) * 3), np.tile(unique_vals, 3), rotation=30, ha='right')