🎨 formatting and plot sizes adjusted

RasmussenLab · Feb 2, 2024 · d0aec7e · d0aec7e
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diff --git a/project/00_5_training_data_exploration.py b/project/00_5_training_data_exploration.py
@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.5
+#       jupytext_version: 1.15.0
 #   kernelspec:
 #     display_name: vaep
 #     language: python
@@ -179,6 +179,7 @@ def get_dynamic_range(min_max):
                    f"{_levels_dropped}")
 # allows overwriting of index name, also to None
 data.columns.name = COL_INDEX_NAME
+data
 
 
 # %% [markdown]

diff --git a/project/01_0_split_data.ipynb b/project/01_0_split_data.ipynb
@@ -50,9 +50,9 @@
     "\n",
     "\n",
     "pd.options.display.max_columns = 32\n",
-    "plt.rcParams['figure.figsize'] = [4, 2]\n",
+    "plt.rcParams['figure.figsize'] = [3, 2]\n",
     "\n",
-    "vaep.plotting.make_large_descriptors(6)\n",
+    "vaep.plotting.make_large_descriptors(7)\n",
     "\n",
     "figures = {}  # collection of ax or figures\n",
     "dumps = {}  # collection of data dumps"
@@ -820,12 +820,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ax = df_w_date.boxplot(rot=80,\n",
-    "                       figsize=(8, 3),\n",
-    "                       fontsize=6,\n",
-    "                       showfliers=False,\n",
-    "                       showcaps=False\n",
-    "                       )\n",
+    "ax = df_w_date.plot.box(rot=80,\n",
+    "                        figsize=(7, 3),\n",
+    "                        fontsize=7,\n",
+    "                        showfliers=False,\n",
+    "                        showcaps=False,\n",
+    "                        boxprops=dict(linewidth=.4, color='darkblue'),\n",
+    "                        flierprops=dict(markersize=.4, color='lightblue'),\n",
+    "                        )\n",
     "_ = vaep.plotting.select_xticks(ax)\n",
     "fig = ax.get_figure()\n",
     "fname = params.out_figures / f'0_{group}_median_boxplot'\n",
@@ -1004,7 +1006,7 @@
     "group = 2\n",
     "! move parameter checks to start of script\n",
     "if 0.0 <= params.frac_mnar <= 1.0:\n",
-    "    fig, axes = plt.subplots(1, 2, figsize=(8, 2))\n",
+    "    fig, axes = plt.subplots(1, 2, figsize=(6, 2))\n",
     "    quantile_frac = df_long.quantile(params.frac_non_train)\n",
     "    rng = np.random.default_rng(params.random_state)\n",
     "    threshold = pd.Series(rng.normal(loc=float(quantile_frac),\n",
@@ -1404,7 +1406,7 @@
     "medians = medians.join(feat_with_median, on='median_floor')\n",
     "medians = medians.apply(lambda s: \"{:02,d} (N={:3,d})\".format(*s), axis=1)\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(8, 2))\n",
+    "fig, ax = plt.subplots(figsize=(6, 2))\n",
     "s = 1\n",
     "s_axes = pd.DataFrame({'medians': medians,\n",
     "                       'validation split': splits.val_y.notna().sum(),\n",

diff --git a/project/01_0_split_data.py b/project/01_0_split_data.py
@@ -54,9 +54,9 @@ def add_meta_data(df: pd.DataFrame, df_meta: pd.DataFrame):
 
 
 pd.options.display.max_columns = 32
-plt.rcParams['figure.figsize'] = [4, 2]
+plt.rcParams['figure.figsize'] = [3, 2]
 
-vaep.plotting.make_large_descriptors(6)
+vaep.plotting.make_large_descriptors(7)
 
 figures = {}  # collection of ax or figures
 dumps = {}  # collection of data dumps
@@ -507,12 +507,14 @@ def join_as_str(seq):
 df_w_date
 
 # %%
-ax = df_w_date.boxplot(rot=80,
-                       figsize=(8, 3),
-                       fontsize=6,
-                       showfliers=False,
-                       showcaps=False
-                       )
+ax = df_w_date.plot.box(rot=80,
+                        figsize=(7, 3),
+                        fontsize=7,
+                        showfliers=False,
+                        showcaps=False,
+                        boxprops=dict(linewidth=.4, color='darkblue'),
+                        flierprops=dict(markersize=.4, color='lightblue'),
+                        )
 _ = vaep.plotting.select_xticks(ax)
 fig = ax.get_figure()
 fname = params.out_figures / f'0_{group}_median_boxplot'
@@ -612,7 +614,7 @@ def join_as_str(seq):
 group = 2
 # ! move parameter checks to start of script
 if 0.0 <= params.frac_mnar <= 1.0:
-    fig, axes = plt.subplots(1, 2, figsize=(8, 2))
+    fig, axes = plt.subplots(1, 2, figsize=(6, 2))
     quantile_frac = df_long.quantile(params.frac_non_train)
     rng = np.random.default_rng(params.random_state)
     threshold = pd.Series(rng.normal(loc=float(quantile_frac),
@@ -890,7 +892,7 @@ def join_as_str(seq):
 medians = medians.join(feat_with_median, on='median_floor')
 medians = medians.apply(lambda s: "{:02,d} (N={:3,d})".format(*s), axis=1)
 
-fig, ax = plt.subplots(figsize=(8, 2))
+fig, ax = plt.subplots(figsize=(6, 2))
 s = 1
 s_axes = pd.DataFrame({'medians': medians,
                        'validation split': splits.val_y.notna().sum(),

diff --git a/project/01_2_performance_plots.ipynb b/project/01_2_performance_plots.ipynb
@@ -49,8 +49,8 @@
     "pd.options.display.min_rows = 10\n",
     "pd.options.display.max_colwidth = 100\n",
     "\n",
-    "plt.rcParams.update({'figure.figsize': (4, 2)})\n",
-    "vaep.plotting.make_large_descriptors(6)\n",
+    "plt.rcParams.update({'figure.figsize': (3, 2)})\n",
+    "vaep.plotting.make_large_descriptors(7)\n",
     "\n",
     "logger = vaep.logging.setup_nb_logger()\n",
     "logging.getLogger('fontTools').setLevel(logging.WARNING)\n",
@@ -1256,16 +1256,17 @@
    },
    "outputs": [],
    "source": [
-    "fig, ax = plt.subplots(figsize=(4, 2))\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 2))\n",
     "ax = _to_plot.loc[[feature_names.name]].plot.bar(\n",
     "    rot=0,\n",
     "    ylabel=f\"{METRIC} for {FEAT_NAME_DISPLAY} ({n_in_comparison:,} intensities)\",\n",
     "    # title=f'performance on test data (based on {n_in_comparison:,} measurements)',\n",
     "    color=COLORS_TO_USE,\n",
     "    ax=ax,\n",
     "    width=.8)\n",
-    "ax = vaep.plotting.add_height_to_barplot(ax, size=5)\n",
-    "ax = vaep.plotting.add_text_to_barplot(ax, _to_plot.loc[\"text\"], size=5)\n",
+    "ax = vaep.plotting.add_height_to_barplot(ax, size=7)\n",
+    "ax = vaep.plotting.add_text_to_barplot(ax, _to_plot.loc[\"text\"], size=7)\n",
     "ax.set_xticklabels([])\n",
     "fname = args.out_figures / f'2_{group}_performance_test.pdf'\n",
     "figures[fname.stem] = fname\n",
@@ -1309,7 +1310,7 @@
    "outputs": [],
    "source": [
     "vaep.plotting.make_large_descriptors(7)\n",
-    "fig, ax = plt.subplots(figsize=(8, 2))\n",
+    "fig, ax = plt.subplots(figsize=(6, 2))\n",
     "\n",
     "ax, errors_binned = vaep.plotting.errors.plot_errors_by_median(\n",
     "    pred=pred_test[\n",

diff --git a/project/01_2_performance_plots.py b/project/01_2_performance_plots.py
@@ -49,8 +49,8 @@
 pd.options.display.min_rows = 10
 pd.options.display.max_colwidth = 100
 
-plt.rcParams.update({'figure.figsize': (4, 2)})
-vaep.plotting.make_large_descriptors(6)
+plt.rcParams.update({'figure.figsize': (3, 2)})
+vaep.plotting.make_large_descriptors(7)
 
 logger = vaep.logging.setup_nb_logger()
 logging.getLogger('fontTools').setLevel(logging.WARNING)
@@ -636,16 +636,17 @@ def highlight_min(s, color, tolerence=0.00001):
 
 
 # %%
-fig, ax = plt.subplots(figsize=(4, 2))
+
+fig, ax = plt.subplots(figsize=(6, 2))
 ax = _to_plot.loc[[feature_names.name]].plot.bar(
     rot=0,
     ylabel=f"{METRIC} for {FEAT_NAME_DISPLAY} ({n_in_comparison:,} intensities)",
     # title=f'performance on test data (based on {n_in_comparison:,} measurements)',
     color=COLORS_TO_USE,
     ax=ax,
     width=.8)
-ax = vaep.plotting.add_height_to_barplot(ax, size=5)
-ax = vaep.plotting.add_text_to_barplot(ax, _to_plot.loc["text"], size=5)
+ax = vaep.plotting.add_height_to_barplot(ax, size=7)
+ax = vaep.plotting.add_text_to_barplot(ax, _to_plot.loc["text"], size=7)
 ax.set_xticklabels([])
 fname = args.out_figures / f'2_{group}_performance_test.pdf'
 figures[fname.stem] = fname
@@ -666,7 +667,7 @@ def highlight_min(s, color, tolerence=0.00001):
 
 # %%
 vaep.plotting.make_large_descriptors(7)
-fig, ax = plt.subplots(figsize=(8, 2))
+fig, ax = plt.subplots(figsize=(6, 2))
 
 ax, errors_binned = vaep.plotting.errors.plot_errors_by_median(
     pred=pred_test[

diff --git a/project/02_3_grid_search_analysis.ipynb b/project/02_3_grid_search_analysis.ipynb
@@ -16,6 +16,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import logging\n",
     "import pathlib\n",
     "import pandas as pd\n",
     "import plotly.express as px\n",
@@ -38,7 +39,8 @@
     "pd.options.display.max_rows = 100\n",
     "pd.options.display.multi_sparse = False\n",
     "\n",
-    "logger = vaep.logging.setup_nb_logger()"
+    "logger = vaep.logging.setup_nb_logger()\n",
+    "logging.getLogger('fontTools').setLevel(logging.WARNING)"
    ]
   },
   {
@@ -603,15 +605,13 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "f8190d51-c4db-4aae-8b91-11641958a0f8",
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "plt.rcParams['figure.figsize'] = (8, 4)\n",
+    "plt.rcParams['figure.figsize'] = (7, 4)\n",
     "plt.rcParams['lines.linewidth'] = 2\n",
     "plt.rcParams['lines.markersize'] = 3\n",
-    "vaep.plotting.make_large_descriptors(5)\n",
+    "vaep.plotting.make_large_descriptors(7)\n",
     "\n",
     "col_order = ('valid_fake_na', 'test_fake_na')\n",
     "row_order = ('MAE', 'MSE')\n",
@@ -628,24 +628,29 @@
     "    palette=vaep.plotting.defaults.color_model_mapping,\n",
     "    height=2,\n",
     "    aspect=1.8,\n",
-    "    kind=\"scatter\"\n",
+    "    kind=\"scatter\",\n",
     ")\n",
-    "fg.fig.get_size_inches()\n",
     "\n",
     "(ax_00, ax_01), (ax_10, ax_11) = fg.axes\n",
     "ax_00.set_ylabel(row_order[0])\n",
     "ax_10.set_ylabel(row_order[1])\n",
     "_ = ax_00.set_title('validation data')  # col_order[0]\n",
     "_ = ax_01.set_title('test data')  # col_order[1]\n",
+    "ax_10.set_xticklabels(ax_10.get_xticklabels(),\n",
+    "                      rotation=45,\n",
+    "                      horizontalalignment='right')\n",
     "ax_10.set_xlabel('number of parameters')  # n_params\n",
+    "ax_11.set_xticklabels(ax_11.get_xticklabels(),\n",
+    "                      rotation=45,\n",
+    "                      horizontalalignment='right')\n",
     "ax_11.set_xlabel('number of parameters')\n",
     "ax_10.xaxis.set_major_formatter(\"{x:,.0f}\")\n",
     "ax_11.xaxis.set_major_formatter(\"{x:,.0f}\")\n",
     "_ = ax_10.set_title('')\n",
     "_ = ax_11.set_title('')\n",
     "fg.tight_layout()\n",
     "fname\n",
-    "fname = FOLDER / f\"hyperpar_results_by_parameters_val+test.pdf\"\n",
+    "fname = FOLDER / \"hyperpar_results_by_parameters_val+test.pdf\"\n",
     "files_out[fname.name] = fname.as_posix()\n",
     "fg.savefig(fname)\n",
     "fg.savefig(fname.with_suffix('.png'), dpi=300)"
@@ -658,6 +663,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
+    "\n",
     "def plot_by_params(data_split: str = '', subset: str = ''):\n",
     "    selected = metrics_long\n",
     "    if data_split:\n",
@@ -1107,13 +1114,16 @@
    "outputs": [],
    "source": [
     "mask = errors_smoothed[freq_feat.name] >= FREQ_MIN\n",
-    "ax = errors_smoothed.loc[mask].rename_axis('', axis=1).plot(x=freq_feat.name,\n",
-    "                                                            xlabel='freq/feature prevalence (across samples)',\n",
-    "                                                            ylabel=f'rolling average error ({METRIC})',\n",
-    "                                                            xlim=(\n",
-    "                                                                FREQ_MIN, errors_smoothed[freq_feat.name].max()),\n",
-    "                                                            # title=f'Rolling average error by feature frequency {msg_annotation}'\n",
-    "                                                            )\n",
+    "ax = (errors_smoothed\n",
+    "      .loc[mask]\n",
+    "      .rename_axis('', axis=1)\n",
+    "      .plot(x=freq_feat.name,\n",
+    "            xlabel='freq/feature prevalence (across samples)',\n",
+    "            ylabel=f'rolling average error ({METRIC})',\n",
+    "            xlim=(\n",
+    "                FREQ_MIN, errors_smoothed[freq_feat.name].max()),\n",
+    "            # title=f'Rolling average error by feature frequency {msg_annotation}'\n",
+    "            ))\n",
     "\n",
     "msg_annotation = f\"(Latend dim: {min_latent}, No. of feat: {M_feat}, window_size: {window_size})\"\n",
     "print(msg_annotation)\n",
@@ -1155,7 +1165,10 @@
    },
    "outputs": [],
    "source": [
-    "fig = px_vaep.line(errors_smoothed_long.loc[errors_smoothed_long[freq_feat.name] >= FREQ_MIN].join(n_obs_error_is_based_on).sort_values(by='freq'),\n",
+    "fig = px_vaep.line((errors_smoothed_long\n",
+    "                    .loc[errors_smoothed_long[freq_feat.name] >= FREQ_MIN]\n",
+    "                    .join(n_obs_error_is_based_on)\n",
+    "                    .sort_values(by='freq')),\n",
     "                   x=freq_feat.name,\n",
     "                   color='model',\n",
     "                   y='rolling error average',\n",
@@ -1424,13 +1437,16 @@
     "errors_smoothed[order_models] = errors[order_models].rolling(\n",
     "    window=window_size, min_periods=1).mean()\n",
     "mask = errors_smoothed[freq_feat.name] >= FREQ_MIN\n",
-    "ax = errors_smoothed.loc[mask].rename_axis('', axis=1).plot(x=freq_feat.name,\n",
-    "                                                            ylabel='rolling error average',\n",
-    "                                                            xlabel='freq/feature prevalence (across samples)',\n",
-    "                                                            xlim=(\n",
-    "                                                                FREQ_MIN, freq_feat.max()),\n",
-    "                                                            # title=f'Rolling average error by feature frequency {msg_annotation}'\n",
-    "                                                            )\n",
+    "ax = (errors_smoothed\n",
+    "      .loc[mask]\n",
+    "      .rename_axis('', axis=1)\n",
+    "      .plot(x=freq_feat.name,\n",
+    "            ylabel='rolling error average',\n",
+    "            xlabel='freq/feature prevalence (across samples)',\n",
+    "            xlim=(\n",
+    "                FREQ_MIN, freq_feat.max()),\n",
+    "            # title=f'Rolling average error by feature frequency {msg_annotation}'\n",
+    "            ))\n",
     "\n",
     "vaep.savefig(\n",
     "    ax.get_figure(),\n",