""" ============================== 1. Exploratory Data Analysis ============================== """ import matplotlib.pyplot as plt from ai4water.eda import EDA from ai4water.utils import TrainTestSplit from easy_mpl import hist from easy_mpl import plot from easy_mpl import scatter from easy_mpl import boxplot from easy_mpl.utils import create_subplots from easy_mpl.utils import map_array_to_cmap, process_cbar from utils import read_data from utils import COLUMN_MAPS from utils import set_rcParams, version_info # %% for k,v in version_info().items(): print(k, v) # %% set_rcParams() # %% COLUMN_MAPS_ = {v:k for k,v in COLUMN_MAPS.items()} COLUMN_MAPS_['ww_conc'] = "Wastewater Conc." COLUMN_MAPS_['sonic_pd'] = "Sonicator Power" COLUMN_MAPS_['h20_conc.'] = 'H2O2 Conc.' # %% data = read_data() data_area = read_data(target='Area (ABD) Mean') data_both = read_data(target=['Area (ABD) Mean', 'Efficiency']) print(data.shape) # %% data_both.describe() # %% eda = EDA(data=data, save=False, show=False) ax = eda.correlation(figsize=(8,8), square=True, cbar_kws={"shrink": .72}, cmap="Spectral" ) ax.set_xticklabels(ax.get_xticklabels(), fontsize=12, weight='bold', rotation=70) ax.set_yticklabels(ax.get_yticklabels(), fontsize=12, weight='bold') plt.tight_layout() plt.show() # %% # Correlation # =============== eda = EDA(data=data_area, save=False, show=False) ax = eda.correlation(figsize=(8,8), square=True, cbar_kws={"shrink": .72}, cmap="Spectral" ) ax.set_xticklabels(ax.get_xticklabels(), fontsize=12, weight='bold', rotation=70) ax.set_yticklabels(ax.get_yticklabels(), fontsize=12, weight='bold') plt.tight_layout() plt.show() # %% eda = EDA(data=data_both, save=False, show=False) ax = eda.correlation(figsize=(8,8), square=True, cbar_kws={"shrink": .72}, cmap="Spectral" ) ax.set_xticklabels(ax.get_xticklabels(), fontsize=12, weight='bold', rotation=70) ax.set_yticklabels(ax.get_yticklabels(), fontsize=12, weight='bold') plt.tight_layout() plt.show() # %% # Distribution # ============== f, axes = create_subplots(data_both.shape[1], sharex="all") for col, ax in zip(data_both.columns, axes.flatten()): boxplot( data_both[col], labels=col, ax=ax, show=False, fill_color="lightpink", patch_artist=True, widths=0.7, flierprops=dict(ms=2.0), medianprops={"color": "black"}, ) ax.set_xlabel(col) plt.tight_layout() plt.show() # %% train_count, test_count, _, _ = TrainTestSplit(seed=313).split_by_random( data['Efficiency'], ) ax, _ = boxplot([train_count, test_count], flierprops=dict(ms=2.0), widths=0.6, labels=["Train", "Test"], showmeans=True, patch_artist=True, fill_color=["darkorange", "peachpuff"], medianprops={"color": "black", 'linewidth': 2}, capprops={"linewidth":2}, whiskerprops=dict(linewidth=2), line_width=2., meanprops={"markerfacecolor": "black", "markeredgecolor": 'black', "marker": "o"}, show=False ) ax.tick_params(labelsize=12) ax.set_xticklabels(["Train", "Test"], fontsize=12) ax.grid(visible=True, ls='--', color='lightgrey') plt.show() # %% _ = hist([train_count.values, test_count.values], labels=["Train", "Test"], alpha=0.7) # %% train_area, test_area, _, _ = TrainTestSplit(seed=313).split_by_random( data_area['Area (ABD) Mean'], ) ax, _ = boxplot([train_area, test_area], flierprops=dict(ms=2.0), widths=0.6, labels=["Train", "Test"], showmeans=True, patch_artist=True, fill_color=["darkorange", "peachpuff"], medianprops={"color": "black", 'linewidth': 2}, capprops={"linewidth":2}, whiskerprops=dict(linewidth=2), line_width=2., meanprops={"markerfacecolor": "black", "markeredgecolor": 'black', "marker": "o"}, show=False ) ax.tick_params(labelsize=12) ax.set_xticklabels(["Train", "Test"], fontsize=12) ax.grid(visible=True, ls='--', color='lightgrey') plt.show() # %% _ = hist([train_area.values, test_area.values], labels=["Train", "Test"], alpha=0.7) # %% # line plot # =========== fig, axes = create_subplots(data_both.shape[1]) for ax, col, label in zip(axes.flat, data_both, data_both.columns): plot(data_both[col].values, ax=ax, ax_kws=dict(ylabel=COLUMN_MAPS_.get(col, col), ylabel_kws={"fontsize": 12, 'weight': 'bold'},), lw=0.9, color='darkcyan', show=False) plt.tight_layout() plt.show() # %% # Feature Interaction # ==================== def draw_scatter(target, ax, label="Efficiency"): ax.grid(visible=True, ls='--', color='lightgrey') c, mapper = map_array_to_cmap(data[target].values, "inferno") if target in ["Sonic. PD", "Volume (mL)"]: ylabel = None else: ylabel = label if target in ['Solution pH', 'Volume (mL)']: xlabel = "Time (min)" else: xlabel = None ax_, _ = scatter(data_both['Time (min)'], data_both[label], color=c, alpha=0.5, s=40, ec="grey", zorder=10, ax_kws=dict(logy=True, ylabel=ylabel, ylabel_kws={"fontsize": 12, 'weight': 'bold'}, top_spine=False, right_spine=False, xlabel=xlabel, xlabel_kws={"fontsize": 12, 'weight': 'bold'}), ax=ax, show=False) process_cbar(ax_, mappable=mapper, orientation="vertical", pad=0.1, border=False, title=COLUMN_MAPS_.get(target, target), title_kws=dict(fontsize=12)) return f, all_axes = create_subplots(5, sharex="all", facecolor="#EFE9E6", figsize=(9, 6)) targets = ['Ini. CC', 'Sonic. PD', 'h20 Conc.', 'Volume (mL)', 'Solution pH'] for col, axes in zip(targets, all_axes.flatten()): draw_scatter(col, axes) plt.tight_layout() plt.show() # %% f, all_axes = create_subplots(5, sharex="all", facecolor="#EFE9E6", figsize=(9, 6)) targets = ['Ini. CC', 'Sonic. PD', 'h20 Conc.', 'Volume (mL)', 'Solution pH'] for col, axes in zip(targets, all_axes.flatten()): draw_scatter(col, axes, label="Area (ABD) Mean") plt.tight_layout() plt.show()